Findings & Analysis

Published

June 20, 2025

Modified

June 28, 2025

1 Exploratory Data Analysis (EDA) on Sailor Shift’s career

We will design and develop visualizations and visual analytic tools that will allow Silas to explore and understand the profile of Sailor Shift’s career.

Note

This analysis examines Sailor Shift’s work during her solo artist career, excluding her initial collaborative period with Ivy Echos.

Future work: What if we make everything into a function and find members that the person is part of and use a while loop to get everything so it includes all groups that the person is a part of?

1.1 Visualising Sailor Shift’s Music

The analysis begins by extracting Sailor Shift’s name from the node dataset. Following this, all outgoing edges originating from her node are identified. The resulting nodes from those edges are filtered to isolate only those classified as Songs and Albums, thereby displaying her musical career.

Show Code
# Step 0: Get name of 'Sailor Shift'
sailor_vertex_name <- mc1_nodes_clean %>%
  filter(is_sailor == TRUE) %>%
  pull(name)

# Step 1: Find outgoing edges from Sailor Shift
sailor_out_edges <- mc1_edges_clean %>%
  filter(from == sailor_vertex_name)

# Step 2: Identify neighbour node names
sailor_out_node_names <- sailor_out_edges$to

# Step 4: Identify songs/albums
sailor_music_all <- mc1_nodes_clean %>%
  filter(name %in% sailor_out_node_names, `Node Type` %in% c("Song", "Album")) %>%
  pull(name)

# Step 5: Build subgraph using names
sub_nodes <- unique(c(sailor_vertex_name, sailor_music_all))

sub_graph <- graph %>%
  activate(nodes) %>%
  filter(name %in% sub_nodes)



# Visualisation

g <- sub_graph %>%
  ggraph(layout = "fr") + 
  geom_edge_fan(
    aes(
      edge_colour = `Edge Colour`,
      start_cap = circle(1, 'mm'),
      end_cap = circle(1, 'mm')
      ),
    arrow = arrow(length = unit(1, 'mm')),
    alpha = 0.3
  ) +
  geom_point_interactive(
    aes(
      x = x,
      y = y,
      data_id = name,
      colour = `Node Colour`,
      shape = `Node Type`,
      size = ifelse(node_name %in% c("Sailor Shift", "Ivy Echos", "Wei Zhao"), 3, 1),
      tooltip = case_when(
        `Node Type` == "Album" ~ sprintf(
          "%s<br/>%s<br/>Notable: %s<br/>(%s)", node_name, genre, notable, release_date
        ),
        `Node Type` == "Song" ~ sprintf(
          "%s<br/>%s<br/>Notable: %s<br/>(%s)<br/>Single: %s", node_name, genre, notable, release_date, single
        ),
        TRUE ~ sprintf("%s", node_name)
      )
    ),
    show.legend = c(size = FALSE)
  )+ 
  geom_node_text(
    aes(
      label = ifelse(node_name == "Sailor Shift", "Sailor Shift",
              ifelse(node_name == "Ivy Echos", "Ivy Echos",
              ifelse(node_name == "Wei Zhao", "Wei Zhao", NA)))
    ),
    fontface = "bold",
    size = 2.5,
    colour = 'red',
    show.legend = FALSE
  ) +
  scale_shape_manual(
    name = "Node Type",
    values = c(
      "Album" = 16,
      "MusicalGroup" = 15,
      "Person" = 17,
      "Song" = 10
    )
  ) +
  scale_edge_colour_manual(
    name = "Edge Colour",
    values = c(
      `Creator Of` = "#47D45A",
      `Influenced By` = "#FF5757",
      `Member Of` = "#CF57FF"
    )
  ) +
  scale_colour_manual(
    name = "Node Colour",
    values = c(
      "Musician" = "grey50",
      "Oceanus Folk" = "#0027EA",
      "Other Genre" = "#A45200"
    )
  ) +
  theme_graph() +
  theme(legend.text = element_text(size = 6),
        legend.title = element_text(size = 9)) +
  scale_size_identity()

girafe(ggobj = g, width_svg = 7, height_svg = 6)
Show Code
# Prepare data
sailor_release_data <- mc1_nodes_clean %>%
  filter(name %in% sailor_music_all) %>%
  count(release_date, genre)

# Plot
ggplot(sailor_release_data, aes(x = release_date, y = n, fill = genre)) +
  geom_col() +
  geom_vline(xintercept = 2028, linetype = "dashed", color = "red") +
    annotate("text", 
           x = 2028, 
           y = max(sailor_release_data$n) + 0.5,
           label = "Viral Breakthrough", 
           color = "red", 
           size = 3.5,
           angle = 0,        # <- horizontal
           vjust = -0.5,     # <- moves it slightly above
           hjust = 0.5       # <- centers it horizontally
  ) +
  scale_fill_brewer(palette = "Pastel1") +
  labs(
    title = "Sailor Shift's Music Releases (2024–2040)",
    x = "Year",
    y = "Number of Songs/Albums",
    fill = "Genre"
  ) +
  theme_minimal()

Sailor Shift’s Music Insights
  • In total, Sailor Shift released 38 Songs and Albums from 2024 to 2040
  • Details on the Songs and Albums can be found in the table below
  • Sailor Shift released 4 Songs in 2028
    • Among those, only 1 is a Single
    • Therefore, the viral Song that spark her breakthrough is High Tide Heartbeat
  • Among her Songs and Albums, 36 belong to the Oceanus Folk genre, while there is 1 Americana and 1 Synthwave Album each.
Show Code
mc1_nodes_clean %>%
  filter(name %in% sailor_music_all) %>%
  arrange(release_date) %>%
  select(`Node Type`, node_name, release_date, genre, notable, single, notoriety_date) %>%
  kable() %>%
  kable_styling("striped", full_width = F) %>% 
  scroll_box(height = "300px")
Node Type node_name release_date genre notable single notoriety_date
Album The Kelp Forest Canticles 2024 Oceanus Folk TRUE NA NA
Album Luminescent Tides 2025 Oceanus Folk TRUE NA NA
Album Shoreline Sonnets 2026 Oceanus Folk TRUE NA NA
Album Tidal Pop Waves 2028 Oceanus Folk TRUE NA NA
Song Chord of the Deep 2028 Oceanus Folk FALSE FALSE NA
Song Electric Eel Love 2028 Oceanus Folk TRUE FALSE NA
Song High Tide Heartbeat 2028 Oceanus Folk FALSE TRUE NA
Song Sun-Drenched Daydream 2028 Oceanus Folk FALSE FALSE NA
Album Tides of Echos 2029 Oceanus Folk TRUE NA NA
Album Salty Dreams 2030 Oceanus Folk TRUE NA NA
Song Driftwood Lullaby 2030 Oceanus Folk FALSE FALSE NA
Song Heart of the Habitat 2030 Oceanus Folk FALSE TRUE NA
Song Reef Rhythm 2030 Oceanus Folk FALSE FALSE NA
Song Seashell Serenade 2030 Oceanus Folk TRUE TRUE 2030
Album Hidden Depths 2031 Oceanus Folk TRUE NA NA
Album Neon Heartbeat 2031 Synthwave FALSE NA NA
Album Submerged Sonatas 2031 Oceanus Folk TRUE NA NA
Album Tidesworn Ballads 2031 Oceanus Folk TRUE NA NA
Album The Current & The Chord 2032 Oceanus Folk TRUE NA NA
Song Saltwater Hymn 2032 Oceanus Folk FALSE FALSE NA
Album Ballads for the End of Time 2033 Oceanus Folk TRUE NA NA
Album Melancholy Circuitry 2033 Americana TRUE NA NA
Album Coral Beats 2034 Oceanus Folk TRUE NA NA
Album Drifting Between the Stars and the Sea 2034 Oceanus Folk TRUE NA NA
Song Barnacle Heart 2034 Oceanus Folk FALSE FALSE NA
Song Into the Current 2034 Oceanus Folk FALSE TRUE NA
Song Moon Over the Tide 2034 Oceanus Folk TRUE FALSE NA
Album Artificial Sunsets 2035 Oceanus Folk TRUE NA NA
Album Tides & Ballads 2036 Oceanus Folk TRUE NA NA
Song Fog & Fiddle 2036 Oceanus Folk TRUE FALSE NA
Song The Fisherman's Prayer 2036 Oceanus Folk FALSE TRUE NA
Album Ballads for the Low Tide 2037 Oceanus Folk TRUE NA NA
Album Electric Reverie 2038 Oceanus Folk TRUE NA NA
Album Oceanbound 2038 Oceanus Folk TRUE NA NA
Song Stormsong 2038 Oceanus Folk TRUE TRUE NA
Album Echoes of the Deep 2040 Oceanus Folk TRUE NA NA
Song Salt in My Veins 2040 Oceanus Folk FALSE FALSE NA
Song The Last Mariner 2040 Oceanus Folk FALSE FALSE NA

2 Profile of Sailor Shift’s Career

2.1 Part 1a) Who has Sailor Shift been most influenced by over time?

This section builds on the analysis of Sailor Shift’s outgoing edges. After identifying her Songs and Albums, it expands to include associated Persons and Musical Groups she directly influenced. Among these, only the Songs and Albums with further outgoing influence are retained. The next step traces the music that Sailor’s works influenced, and identifies the creators (i.e. Persons and Musical Groups) behind the influencing works.

Note: The term music/works refer to either Song or Album.

Show Code
# Data Preparation

# Step 1: Get all creator names
global_creators <- mc1_nodes_clean %>%
  filter(`Node Type` %in% c("Person", "MusicalGroup"))

# Step 2: Get all outgoing edges from these creators
creator_out_edges <- mc1_edges_clean %>%
  filter(from %in% global_creators$name, `Edge Colour` == "Creator Of")

# Step 3: Get all songs made by creators
creator_music <- mc1_nodes_clean %>%
  filter(name %in% creator_out_edges$to)

# Step 4: Get all outgoing edges from songs
creator_songs_out_edges <- mc1_edges_clean %>%
  filter(from %in% creator_music$name, `Edge Colour` == "Influenced By")

# Step 5: First join to get creator names (from)
creators <- creator_out_edges %>%
  left_join(mc1_nodes_clean, by = c("from" = "name")) %>%
  select(from, to, node_name, `Node Type`) %>%
  rename(creator_from = from, creator_name = node_name, creator_node_type = `Node Type`)

# Step 6: Second join to get song names (to)
creator_and_songs <- creators %>%
  left_join(mc1_nodes_clean, by = c("to" = "name")) %>%
  select(creator_from, creator_name, creator_node_type, to, node_name, release_date, genre, notable) %>%
  rename(song_name = node_name, song_genre = genre, song_to = to) %>%
  distinct()

# Step 7: Third join to get song's influenced genre (to)
creator_and_songs_and_influenced_by <- creator_and_songs %>%
  left_join(creator_songs_out_edges %>% select(from, to), by = c("song_to" = "from"), relationship = "many-to-many") %>%
  left_join(mc1_nodes_clean %>% select(name, genre), by = c("to" = "name")) %>%
  rename(influenced_by_genre = genre, influenced_by = to) %>%
  distinct()

# Step 8: Fourth join to get influenced song's creator
creator_and_songs_and_influenced_by_creator <- creator_and_songs_and_influenced_by %>%
  left_join(creator_out_edges %>% select(from, to), by = c("influenced_by" = "to"), relationship = "many-to-many") %>%
  rename(influenced_by_creator = from)
Show Code
# Data Preparation

# Step 1: Get the node of the sailor
sailor_node <- creator_and_songs_and_influenced_by_creator %>%
  filter(creator_name == "Sailor Shift") %>%
  pull(creator_from) %>%
  unique()

# Step 2: Get the songs that the creator produced
sailor_songs <- creator_and_songs_and_influenced_by_creator %>%
  filter(creator_from == sailor_node,
         creator_from != influenced_by) %>%
  pull(song_to)

# Step 3: Get the songs they have influenced by
sailor_songs_influenced <- creator_and_songs_and_influenced_by_creator %>%
  filter(creator_from == sailor_node,
         creator_from != influenced_by) %>%
  pull(influenced_by)

# Step 5: Get the creators of the influenced by songs
sailor_songs_influenced_creators <- creator_and_songs_and_influenced_by_creator %>%
  filter(creator_from == sailor_node,
         creator_from != influenced_by) %>%
  pull(influenced_by_creator)

# Step 5: Combine all relevant node names
all_node_names <- unique(c(
  sailor_node,
  sailor_songs,
  sailor_songs_influenced,
  sailor_songs_influenced_creators
))

# Step 6: Filter graph to relevant nodes only
sub_graph <- graph %>%
  filter(name %in% all_node_names)



# Visualisation

g <- sub_graph %>%
  ggraph(layout = "fr") + 
  geom_edge_fan(
    aes(
      edge_colour = `Edge Colour`,
      start_cap = circle(1, 'mm'),
      end_cap = circle(1, 'mm')
      ),
    arrow = arrow(length = unit(1, 'mm')),
    alpha = 0.3
  ) +
  geom_point_interactive(
    aes(
      x = x,
      y = y,
      data_id = name,
      colour = `Node Colour`,
      shape = `Node Type`,
      size = ifelse(node_name %in% c("Sailor Shift", "Wei Zhao"), 3, 1),
      tooltip = case_when(
        `Node Type` == "Album" ~ sprintf(
          "%s<br/>%s<br/>Notable: %s<br/>(%s)", node_name, genre, notable, release_date
        ),
        `Node Type` == "Song" ~ sprintf(
          "%s<br/>%s<br/>Notable: %s<br/>(%s)<br/>Single: %s", node_name, genre, notable, release_date, single
        ),
        TRUE ~ sprintf("%s", node_name)
      )
    ),
    show.legend = c(size = FALSE)
  )+ 
  geom_node_text(
    aes(
      label = ifelse(node_name == "Sailor Shift", "Sailor Shift",
              ifelse(node_name == "Wei Zhao", "Wei Zhao", NA))
    ),
    fontface = "bold",
    size = 2.5,
    colour = 'red',
    show.legend = FALSE
  ) +
  scale_shape_manual(
    name = "Node Type",
    values = c(
      "Album" = 16,
      "MusicalGroup" = 15,
      "Person" = 17,
      "Song" = 10
    )
  ) +
  scale_edge_colour_manual(
    name = "Edge Colour",
    values = c(
      `Creator Of` = "#47D45A",
      `Influenced By` = "#FF5757",
      `Member Of` = "#CF57FF"
    )
  ) +
  scale_colour_manual(
    name = "Node Colour",
    values = c(
      "Musician" = "grey50",
      "Oceanus Folk" = "#0027EA",
      "Other Genre" = "#A45200"
    )
  ) +
  theme_graph() +
  theme(legend.text = element_text(size = 6),
        legend.title = element_text(size = 9)) +
  scale_size_identity()

girafe(ggobj = g, width_svg = 7, height_svg = 6)
Who has Sailor Shift been most influenced by over time?
  • The table below shows all the Person and Musical Group that have influenced Sailor Shift
  • Through the graph, most of the other Person and Musical Group have only produced a single music that has influenced one of her music.
  • However, Wei Zhao stands out because they have influenced Sailor Shift more than once.
    • Wei Zhao is the producer of Susurros de Passion that has influenced the her album Salty Dreams. Wei Zhao is also the composer of Silent Steps in the Forest’s Embrance that has influenced her song Moon Over the Tide.
  • In conclusion, Ivy Echos and Wei Zhao have been the most influential on Sailor Shift’s music career.
Show Code
# Extract node data
node_data <- as_tibble(sub_graph)

# View all Person and Musical Group that has influenced Sailor Shift
node_data %>%
  filter(name != "Sailor Shift", `Node Type` %in% c("Person", "MusicalGroup")) %>%
  select(`Node Type`, node_name) %>%
  kable() %>%
  kable_styling("striped", full_width = F) %>% 
  scroll_box(height = "200px")
Node Type node_name
MusicalGroup Phantom Roots
MusicalGroup Ursus
Person Amico Luciani
Person Amy Powell
Person Belinda Knappe
Person Brooke Olson
Person Caitlin Miller
Person Cipriano Peranda
Person Daniel Mccormick
Person David Reese
Person Debra Graham
Person Dylan Schwartz
Person Edward Evans
Person Elmo Calbo
Person Fang Ding
Person Gang Shao
Person Guiying Ren
Person Herbert Mcfarland
Person Igor Dyś
Person James Henderson
Person James Medina
Person Jason Aguirre
Person Jeremiah Love
Person Jing Kang
Person Joanna Avery
Person Joel Long
Person John Graves
Person Joseph Ponce
Person Joshua Taylor
Person Juan Shi
Person Juan Zhou
Person Judy Yang
Person Jun Qiao
Person Jun Zhou
Person Kaori Ito
Person Kimberly Estrada
Person Lei Fu
Person Lei Jin
Person Lei Shi
Person Linda Burns
Person Lori Massey
Person Matthew Best
Person Matthew Lane
Person Michael Nixon
Person Min Cao
Person Min Long
Person Ming Huang
Person Ming Long
Person Ming Qiao
Person Ming Ren
Person Ming Zhou
Person Miss Jennifer Williams
Person Molly Gonzalez
Person Na Ye
Person Naoko Sato
Person Nathan Jones
Person Ping Jin
Person Ping Liao
Person Qiang Liu
Person Qiang Yuan
Person Qiang Zhou
Person Rachel Montes
Person Rebecca Wise
Person Robert Woods
Person Ryan Dunlap
Person Sailor Shift
Person Samantha Bullock
Person Sandra Burke
Person Sean Jones
Person Shannon Harvey
Person Shawn Johnson
Person Sheryl Roman
Person Simone Säuberlich
Person Tao Gao
Person Tao Hu
Person Tao Wen
Person Trevor Bass
Person Urszula Stochmal
Person Wei Liang
Person Wei Zhao
Person William Robinson
Person Xia Yuan
Person Xiulan Fang
Person Xiuying Du
Person Yan Dai
Person Yan Li
Person Yan Tang
Person Yang Peng
Person Yang Wan
Person Yang Zhao
Person Yoko Hashimoto
Person Yong Dong
Person Yong Lu
Person Zachary Francis

2.2 Part 1b) Who has Sailor Shift collaborated with and directly or indirectly influenced?

<<<Bel: This part need to simplify further and not reference TOE>>>

The analysis begins with the sailor_music_all dataset from Section 4.1, filtering to retain only music with more than two inward edges. This step eliminates works without collaborators or external influences. Two Song/Albums (“The Current & The Chord” and “Salty Dreams”) have to be manually removed since their inward edges are from Sailor herself and therefore not needed. Next, all inward neighbours (source nodes) of Sailor’s music are identified, representing both influenced works (Songs/Albums) and collaborators (Persons/Musical Groups). After filtering to keep only Songs and Albums, their inward neighbours are examined. This reveals: (1) Persons/Musical Groups who have been influenced by Sailor’s music (Persons/Musical Groups), and (2) secondary influenced works (Songs/Albums) that were impacted by the same sources that were influenced by Sailor’s compositions.

Show Code
# Data Preparation

# Step 1: Get all creator names
global_creators <- mc1_nodes_clean %>%
  filter(`Node Type` %in% c("Person", "MusicalGroup"))

# Step 2: Get all outgoing edges from these creators
creator_out_edges <- mc1_edges_clean %>%
  filter(from %in% global_creators$name, `Edge Colour` == "Creator Of")

# Step 3: Get all songs made by creators
creator_music <- mc1_nodes_clean %>%
  filter(name %in% creator_out_edges$to)

# Step 4: Get all incoming edges from songs (music and collaborators)
creator_songs_in_edges <- mc1_edges_clean %>%
  filter(to %in% creator_music$name)

# Step 5: First join to get creator names (from)
creators <- creator_out_edges %>%
  left_join(mc1_nodes_clean, by = c("from" = "name")) %>%
  select(from, to, node_name, `Node Type`) %>%
  rename(creator_name = node_name, creator_node_type = `Node Type`)

# Step 6: Second join to get song names (to)
creator_and_songs <- creators %>%
  left_join(mc1_nodes_clean, by = c("to" = "name")) %>%
  select(from, creator_name, creator_node_type, to, node_name, release_date, genre, notable) %>%
  rename(creator_from = from, song_name = node_name, creator_release_date = release_date, song_genre = genre, song_to = to) %>%
  distinct()

# Step 7: Third join to get influenced songs / collaborators
creator_and_songs_and_influences <- creator_and_songs %>%
  left_join(creator_songs_in_edges %>% select(from, to, `Edge Colour`), by = c("song_to" = "to"), relationship = "many-to-many") %>%
  left_join(mc1_nodes_clean %>% select(name, genre, node_name, release_date), by = c("from" = "name")) %>%
  rename(influence_genre = genre, infuence_music_collaborate = from, infuence_music_collaborate_name = node_name, influence_release_date = release_date) %>%
  distinct()

# Step 8: Fourth join to get influenced song's creator
creator_and_songs_and_influences_and_creators <- creator_and_songs_and_influences %>%
  left_join(creator_out_edges %>% select(from, to), by = c("infuence_music_collaborate" = "to"), relationship = "many-to-many") %>%
  rename(influence_creator = from)

# Step 9: Fifth join to get influenced song's creator name
creator_and_songs_and_influences_and_creators <- creator_and_songs_and_influences_and_creators %>%
  left_join(mc1_nodes_clean %>% select(name, node_name), by = c("influence_creator" = "name")) %>%
  rename(influence_creator_name = node_name)

# Step 10: Add release date for collaborators
creator_and_songs_and_influences_and_creators_collaborate <- creator_and_songs_and_influences_and_creators %>%
  mutate(
    influence_release_date = case_when(
      `Edge Colour` == "Creator Of" ~ creator_release_date,
      TRUE ~ influence_release_date  # Keeps original value if not "Creator Of"
    ),
    influence_creator = case_when(   # A collaborator can also be considered influenced
      `Edge Colour` == "Creator Of" ~ infuence_music_collaborate,
      TRUE ~ influence_creator
    )
  )
Show Code
# Data Preparation

# Step 2: Get the songs that the sailor produced
sailor_songs <- creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_from == sailor_node,
         infuence_music_collaborate != sailor_node) %>%
  pull(song_to)

# Step 3: Get the songs they have influenced / artists they collaborate with
sailor_songs_collaborate_influence <- creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_from == sailor_node,
         infuence_music_collaborate != sailor_node) %>%
  pull(infuence_music_collaborate)

# Step 4: Get the songs they have influenced
sailor_songs_influence <- creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_from == sailor_node,
         infuence_music_collaborate != sailor_node,
         `Edge Colour` == "Influenced By") %>%
  pull(infuence_music_collaborate)

# Step 5: Get the influenced creators of the influenced songs
sailor_songs_influence_creators <- creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_from == sailor_node,
         influence_creator != sailor_node,
         !is.na(influence_creator),
         infuence_music_collaborate %in% sailor_songs_influence) %>%
  pull(influence_creator)

all_nodes <- unique(c(sailor_node, 
                      sailor_songs, 
                      sailor_songs_collaborate_influence,
                      sailor_songs_influence_creators))

# Create subgraph
sub_graph <- graph %>%
  filter(name %in% all_nodes)



# Visualisation

g <- sub_graph %>%
  ggraph(layout = "fr") + 
  geom_edge_fan(
    aes(
      edge_colour = `Edge Colour`,
      start_cap = circle(1, 'mm'),
      end_cap = circle(1, 'mm')
      ),
    arrow = arrow(length = unit(1, 'mm')),
    alpha = 0.3
  ) +
  geom_point_interactive(
    aes(
      x = x,
      y = y,
      data_id = name,
      colour = `Node Colour`,
      shape = `Node Type`,
      size = ifelse(node_name == "Sailor Shift", 3, 1),
      tooltip = case_when(
        `Node Type` == "Album" ~ sprintf(
          "%s<br/>%s<br/>Notable: %s<br/>(%s)", node_name, genre, notable, release_date
        ),
        `Node Type` == "Song" ~ sprintf(
          "%s<br/>%s<br/>Notable: %s<br/>(%s)<br/>Single: %s", node_name, genre, notable, release_date, single
        ),
        TRUE ~ sprintf("%s", node_name)
      )
    ),
    show.legend = c(size = FALSE)
  )+ 
  geom_node_text(
    aes(
      label = ifelse(node_name == "Sailor Shift", "Sailor Shift", NA)
    ),
    fontface = "bold",
    size = 2.5,
    colour = 'red',
    show.legend = FALSE
  ) +
  scale_shape_manual(
    name = "Node Type",
    values = c(
      "Album" = 16,
      "MusicalGroup" = 15,
      "Person" = 17,
      "Song" = 10
    )
  ) +
  scale_edge_colour_manual(
    name = "Edge Colour",
    values = c(
      `Creator Of` = "#47D45A",
      `Influenced By` = "#FF5757",
      `Member Of` = "#CF57FF"
    )
  ) +
  scale_colour_manual(
    name = "Node Colour",
    values = c(
      "Musician" = "grey50",
      "Oceanus Folk" = "#0027EA",
      "Other Genre" = "#A45200"
    )
  ) +
  theme_graph() +
  theme(legend.text = element_text(size = 6),
        legend.title = element_text(size = 9)) +
  scale_size_identity()

girafe(ggobj = g, width_svg = 7, height_svg = 6)
Who has she collaborated with and directly or indirectly influenced?
  • The graph and table shows everyone that she has collaborated with.
    • Sailor Shift has collaborated with 48 different artists and information on them can be found in the table below.
  • However, Sailor Shift has not directly or indirectly influenced anyone, since none of her music has influenced others.
    • For example, no Songs/Albums has referenced her Songs or Albums.
Show Code
collaboration_count <- mc1_nodes_clean %>%
  filter(name %in% all_nodes, 
         `Node Type` %in% c("Person", "MusicalGroup"),
         name != sailor_node) %>%
  distinct(name) %>%
  nrow()

# Print the formatted message
cat(paste0("Sailor Shift has collaborated with ", collaboration_count, 
           " different Persons and Musical Groups\n"))
Sailor Shift has collaborated with 48 different Persons and Musical Groups
Show Code
mc1_nodes_clean %>%
  filter(name %in% all_nodes, 
         `Node Type` %in% c("Person", "MusicalGroup"),
         name != sailor_node) %>%
  arrange(release_date) %>%
  select(`Node Type`, node_name) %>%
  kable() %>%
  scroll_box(height = "200px")
Node Type node_name
MusicalGroup Crimson Carriage
MusicalGroup Ivy Echos
MusicalGroup Selkie's Hollow
MusicalGroup Siren's Call
MusicalGroup The Brine Choir
MusicalGroup The Wave Riders
MusicalGroup Tidal Reverie
MusicalGroup Violet Engines
Person Aiden Harper
Person Arlo Sterling
Person Astrid Nørgaard
Person Beatrice Albright
Person Cassian Rae
Person Coralia Bellweather
Person Daniel O'Connell
Person Elara May
Person Ethan Clarke
Person Ewan MacCrae
Person Finn McGraw
Person Finn Morgan
Person Fiona Mercer
Person Freya Lindholm
Person Iris Moon
Person Jade Thompson
Person Jonah Calloway
Person Kai Reynolds
Person Kara Lee
Person Levi Holloway
Person Lia Grant
Person Liam O'Sullivan
Person Lila "Lilly" Hartman
Person Lila Rivers
Person Lucas Bennett
Person Lyra Blaze
Person Marin Thorne
Person Maya Jensen
Person Maya Torres
Person Mia Waters
Person Michael Harris
Person Olivia Carter
Person Orion Cruz
Person Rusty Riggins
Person Skylar Brooks
Person Sophie Bennett
Person Sophie Ramirez
Person William Tidewell
Person Zachary Cole
Person Zane Cruz

2.3 Part 1c) How has she influenced collaborators of the broader Oceanus Folk community?

This part is tackled by first identifying all Songs and Albums belonging to the Oceanus Folk Genre and retriving their associated artists (Persons/Musical Groups). Then Sailor Shift’s is identified and her degree of separation is calculated to examine her impact on the broader Oceanus Folk community.

Show Code
genre_creators = creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(song_genre == "Oceanus Folk") %>%
  pull(creator_from)

genre_music = creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(song_genre == "Oceanus Folk") %>%
  pull(song_to)

genre_all_nodes <- unique(c(genre_creators,
                    genre_music))

# Create subgraph
sub_graph <- graph %>%
  filter(name %in% genre_all_nodes)


chosen_degree = 13

# Data Preparation

# Convert to igraph object
sub_igraph <- as.igraph(sub_graph)

# Find Sailor Shift's vertex ID
artist_id <- which(V(sub_igraph)$name == sailor_node)

# Calculate distances from Sailor Shift
distances <- distances(sub_igraph, v = artist_id, mode = "all")

# Convert to tidy format, calculate distances and handle infinite values
distance_df <- tibble(
  name = V(sub_igraph)$name,
  degree = as.numeric(distances[1, ])
) %>%
  mutate(degree = ifelse(is.infinite(degree), NA, degree))

filtered_nodes <- distance_df %>%
  filter(degree <= chosen_degree) %>%
  pull(name)

filtered_nodes <- unique(c(filtered_nodes,
                    sailor_node))

# Create subgraph
filtered_graph <- sub_graph %>%
  filter(name %in% filtered_nodes) %>%
  activate(nodes) %>%
  left_join(distance_df, by = "name")

# Visualisation

g <- filtered_graph %>%
  ggraph(layout = "kk") + 
  geom_edge_fan(
    aes(
      edge_colour = `Edge Colour`,
      start_cap = circle(1, 'mm'),
      end_cap = circle(1, 'mm')
      ),
    arrow = arrow(length = unit(1, 'mm')),
    alpha = 0.3
  ) +
  geom_point_interactive(
    aes(
      x = x,
      y = y,
      data_id = name,
      colour = degree,
      shape = `Node Type`,
      size = ifelse(node_name %in% c("Sailor Shift"), 3, 1),
      tooltip = case_when(
        `Node Type` == "Album" ~ sprintf(
          "%s<br/>%s<br/>Notable: %s<br/>(%s)<br/>Degree: %s", node_name, genre, notable, release_date, degree
        ),
        `Node Type` == "Song" ~ sprintf(
          "%s<br/>%s<br/>Notable: %s<br/>(%s)<br/>Single: %s<br/>Degree: %s", node_name, genre, notable, release_date, single, degree
        ),
        TRUE ~ sprintf("%s<br/>Degree: %s", node_name, degree)
      )
    ),
    show.legend = c(size = FALSE)
  )+ 
  geom_node_text(
    aes(
      label = ifelse(node_name == "Sailor Shift", "Sailor Shift", NA)
    ),
    fontface = "bold",
    size = 3,
    colour = 'red',
    show.legend = FALSE
  ) +
  scale_shape_manual(
    name = "Node Type",
    values = c(
      "Album" = 16,
      "MusicalGroup" = 15,
      "Person" = 17,
      "Song" = 10
    )
  ) +
  scale_edge_colour_manual(
    name = "Edge Colour",
    values = c(
      `Creator Of` = "#47D45A",
      `Influenced By` = "#FF5757",
      `Member Of` = "#CF57FF"
    )
  ) +
  theme_graph() +
  theme(legend.text = element_text(size = 6),
        legend.title = element_text(size = 9)) +
  scale_size_identity() + 
  scale_color_gradientn(
    name = "Degree",
    colours = c("#2E3192", "#FFA757"),
    values = scales::rescale(c(0, 13)),
    na.value = "grey50",
    limits = c(0, 13),
    breaks = 0:13
  )

girafe(ggobj = g, width_svg = 9, height_svg = 8)
How has she influenced collaborators of the broader Oceanus Folk community?
  • This graph is displays a network overview of all People/Musical Groups who have produced Oceanus Folk Songs/Albums.
  • While Sailor Shift is connected to a portion of the Oceanus Folk community, most artists in this genre remain outside her influence network - either as distant connections (3rd to 13th degree) or completely unconnected.
  • In conclusion, Sailor Shift has a moderate influence on the broader Oceanus Folk community since her impact is discernible but not widespread.
Other Insights
  • It seems that the Oceanus Folk community places a strong emphasis on collaboration
  • Rather than simply drawing influence from each other’s work, they prefer collaborating directly
    • Evidenced by the sporadic ‘Influenced By’ connections in the network graph

3 Part 2: Influence of Oceanus Folk

We will develop visualizations that illustrate how the influence of Oceanus Folk has spread through the musical world.

3.1 Part 2a) Was this influence intermittent or did it have a gradual rise?

We will explore these three areas to understand the influence of Oceanus Folk over time:

  1. Yearly number of works influenced by Oceanus Folk Music

  2. Yearly number of Oceanus Folk Music releases

  3. Yearly number of New Oceanus Folk Artists and Influenced Artists

3.1.1 Influence based on number of works that were influenced by Oceanus Folk music

To measure how Oceanus Folk influenced the broader music landscape, we tracked how often songs and albums from other genres were influenced by Oceanus Folk works. These influence relationships were drawn from edge types consisting InStyleOf, CoverOf, InterpolatesFrom, LyricalReferenceTo, and DirectlySamples.

We then focused on edges where an Oceanus Folk song or album is the target (to), and the source (from) is the influenced work. The release date of each influenced work and number of such works for each year were extracted. To reveal long-term trends, cumulative total of influenced works are calculated.

The above approach is reflected in the codes below:

Show Code
# Data Preparation

# Step 1: Get all node names in Oceanus Folk genre
oceanus_nodes <- mc1_nodes_clean %>%
  filter(genre == "Oceanus Folk") %>%
  pull(name)

# Step 2: Count number of influenced music by release date
influence_yearly <- creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(song_to %in% unique(oceanus_nodes),
         `Edge Colour` == "Influenced By",
         infuence_music_collaborate != song_to) %>%
  distinct(infuence_music_collaborate, influence_release_date) %>%
  count(influence_release_date, name = "num_influenced_nodes") %>%
  complete(influence_release_date = 1975:2040, fill = list(num_influenced_nodes = 0)) %>%
  arrange(influence_release_date) %>%  # Ensure dates are in chronological order
  filter(cumsum(num_influenced_nodes) > 0) %>%
  mutate(cumulative_influenced = cumsum(num_influenced_nodes))

3.1.2 Yearly number of Oceanus Folk Music releases

The number of Oceanus Folk Music (Songs/Albums) releases that were released each year are obtained using the codes below:

Show Code
# Data Preparation

# Step 1: Get all node names in Oceanus Folk genre
oceanus_nodes <- mc1_nodes_clean %>%
  filter(genre == "Oceanus Folk") %>%
  pull(name)

# Step 2: Count number of oceanus folk nodes by release date
oceanus_nodes_by_date <- mc1_nodes_clean %>%
  filter(name %in% unique(oceanus_nodes)) %>%
  count(release_date, name = "oceanus_nodes_count") %>%
  arrange(release_date) %>%  # Ensure dates are in chronological order
  mutate(cumulative_count = cumsum(oceanus_nodes_count))

3.1.3 Influence based on number of New Oceanus Folk Artists and New Influenced Artists

We examined the network of people and groups involved in Oceanus Folks music creation and influence. Our analysis begins by identifying all artists (i.e. Person and Musical Group) directly connected to Oceanus Folk music via these edges (PerformerOf, ComposerOf , ProducerOf, LyricistOf ) for Oceanus Folk songs and albums.

In parallel, we traced those influenced by Oceanus Folk: people and groups who created songs or albums influenced by Oceanus works music (i.e. with edges belonging to InStyleOf, InterpolatesFrom, CoverOf, LyricalReferenceTo and DirectlySamples). Similarly, members of musical groups (i.e. collaborators) that created works influenced by Oceanus Folk works were included. The Oceanus Folk artists and influenced artists were only counted once during the year they were first created Oceanus Folk works or were influenced by Oceanus Folk to avoid double counting.

Collectively, they are termed as “New Influenced Artists”. Together, these artists form a comprehensive view of Oceanus Folk’s influence on the music landscape.

Show Code
# Step 1: Count number of influenced artists by release date
creators_by_date <- creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(song_to %in% unique(oceanus_nodes),
         influence_creator != creator_from) %>%
  # Get unique artist-date pairs first
  distinct(influence_creator, influence_release_date) %>%
  # Find first influence date for each artist
  group_by(influence_creator) %>%
  summarize(
    first_influence_date = if(n() > 0) min(influence_release_date) else NA_real_,
    .groups = "drop"
  ) %>%
  # Count new artists by first influence date
  count(first_influence_date, name = "people_count") %>%
  arrange(first_influence_date) %>%
  rename(influence_release_date = first_influence_date) %>%
  # Calculate cumulative unique artists
  mutate(cumulative_count = cumsum(people_count))

The plots summarise the yearly counts of the 3 metrics below:

Show Code
# Step 1: Prepare tidy data frame with yearly counts (not cumulative)
df1_counts <- oceanus_nodes_by_date %>%
  select(year = release_date, value = oceanus_nodes_count) %>%
  mutate(series = "Music Releases")

df2_counts <- influence_yearly %>%
  select(year = influence_release_date, value = num_influenced_nodes) %>%
  mutate(series = "Influenced Songs/Albums")

df3_counts <- creators_by_date %>%
  select(year = influence_release_date, value = people_count) %>%
  mutate(series = "New Influenced Artists")

combined_counts_df <- bind_rows(df1_counts, df2_counts, df3_counts)

# Step 2: Create faceted bar plot with horizontal layout
p <- ggplot(combined_counts_df, aes(x = year, y = value, fill = series)) +
  geom_col(show.legend = FALSE) +
  facet_wrap(~series, nrow = 1, scales = "fixed") +  # horizontal facets
  scale_fill_manual(values = c("Music Releases" = "#ADD8E6", 
                               "Influenced Songs/Albums" = "#F08080", 
                               "New Influenced Artists" = "#C2E0C6")) +
  labs(title = "Yearly Numbers by Category",
       x = NULL, y = "Yearly Count") +
  theme_minimal()+
  theme(legend.position = "none", panel.spacing = unit(1.5, "lines"), plot.title = element_text(hjust = 0.5))  # fully removes legend

# Step 3: Make it interactive
ggplotly(p)

The cumulative trends for the three plots are also visualised as follows:

Show Code
# Step 2: Select and rename columns
df1 <- oceanus_nodes_by_date %>%
  select(year = release_date, value = cumulative_count) %>%
  mutate(series = "Music Releases")

df2 <- influence_yearly %>%
  select(year = influence_release_date, value = cumulative_influenced) %>%
  mutate(series = "Influenced Songs/Albums")

df3 <- creators_by_date %>%
  select(year = influence_release_date, value = cumulative_count) %>%
  mutate(series = "New Influenced Artists")

# Step 3: Combine into one tidy data frame
combined_df <- bind_rows(df1, df2, df3)

# Step 4: Plot
plot_ly(combined_df,
        x = ~year,
        y = ~value,
        color = ~series,
        colors = c("Music Releases" = "#ADD8E6", 
                   "Influenced Songs/Albums" = "#F08080", 
                   "New Influenced Artists" = "#C2E0C6"),
        type = 'scatter',
        mode = 'lines+markers',
        hoverinfo = "text",
        hovertext = ~paste0("Year: ", year, "<br>", series, ": ", value)
) %>%
  layout(
    title = list(text = "Oceanus Folk Influence Over Time (Cumulative)", x = 0.5),
    xaxis = list(title = list(text = ""), dtick = 5),
    yaxis = list(title = "Cumulative Count"),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = 1.15,         # position legend above the plot
      yanchor = "bottom"
    ),
  margin = list(t = 160, b = 80),

    # Add annotations
    annotations = list(
      list(
        x = 2024, 
        y = 800,
        text = "<b>2024: Sailor Shift's Debut</b>",
        xref = "x", yref = "y",
        xanchor = "right",
        showarrow = TRUE,
        arrowhead = 2,
        ax = -30, ay = -40,
        font = list(color = "#2E3192", size = 12)
      ),
      list(
        x = 2028, 
        y = 1120,
        text = "<b>2028: Sailor Shift's Breakthrough</b>",
        xref = "x", yref = "y",
        xanchor = "right",
        showarrow = TRUE,
        arrowhead = 2,
        ax = -30, ay = -40,
        font = list(color = "#2E3192", size = 12)
      )
    ),

    # Add vertical dashed lines
    shapes = list(
      list(
        type = "line",
        x0 = 2024, x1 = 2024,
        y0 = 0, y1 = 800,
        line = list(dash = "dash", color = "grey")
      ),
      list(
        type = "line",
        x0 = 2028, x1 = 2028,
        y0 = 0, y1 = 1120,
        line = list(dash = "dash", color = "grey")
      )
    )
  )

Insights for Influenced Songs/ Album

This chart shows the rise of Oceanus Folk music as a source of influence in the music landscape over time since it was introduced in 1992. The blue bars represent the number of influence works each year and the red line shows the cumulative total showing long-term trends.

From the 1990s through early 2000s, Oceanus Folk had very few influenced work. From 2010s onwards, Oceanus Folk music became more influential, followed by several spikes between 2010 and 2022, showing growing influence of the genre before Sailor Shift’s debut.

In 2023, there was a sharp increase in both the number of yearly influenced works and the cumulative total. The rising wave of Oceanus Folk influenced music had coincided with Sailor Shift’s rise and the influence continued to accelerate with Sailor’s breakthrough until 2031.

After 2031, the influence tapers off, suggesting the genre may have matured or plateaued.

Insights for Music Releases

The chart illustrates the rise of Oceanus Folk music from its origins in 1992 to 2040. The blue bars represent the number of Oceanus Folk songs or albums released each year, highlighting notable spikes especially in the mid-2020s.

The red line shows the cumulative total of Oceanus Folk releases over time, which demonstrates a steady and sustained growth in Oceanus Folk music. This growth before plateauing in 2040, indicating that the genre has matured.

Two key milestones in Sailor Shift’s career - her solo debut in 2024 and breakthrough in 2028 were also mapped in the chart. The significant increases in Oceanus Folk music releases occurred in 2023 and 2026, which were the years immediately before and after her key milestones. This suggests that Sailor Shift’s emergence coincided with a rising wave of Oceanus Folk music, and that her success helped to fuel the genre’s growth in the subsequent years.

Insights for New Influenced Artists

The number of new influenced artists began to rise steadily from 2020s, followed by a sharp surge in 2023, followed by Sailor Shift’s debut shortly.

Thereafter, the next peak occured in 2026 and 2028, aligning with Sailor Shift’s breakthrough. This suggests her success was a powerful catalyst for wider influence of Oceanus Folk, inspiring a wave of emerging new artists .

After 2030, the rate of new influenced artists begins to flatten, suggesting that Oceanus Folk had reached saturation in influence.

3.1.4 Bayesian Surprise

We have analysed the Oceanus Folks music influence trends over time using line and bar charts. Besides visual plots, we can quantify if the influence was intermittent or gradual using the statistical tool - Bayesian Surprise.

Bayesian Surprise is a concept in information theory, which is used to identify moments of unexpected change in sequential data. Based our research, it is suitable for analysing trends like the spread of musical influence and temporal pattern detection.

To compute Bayesian Surprise, we treat each year as a new observation and compare the current year’s count to the prior expectation which is modelled using a Poisson probability distribution which is suitable for our count data across years.

These are the steps in generating the Surprise Score:

  • Obtain the yearly time series data from the previous 3 analysis.

  • Model prior belief based on previous year(s)

  • Compute posterior with the new data

  • Calculate Bayesian Surprise as the KL divergence (Kullback–Leibler divergence) between the prior and posterior

The Surprise Computation Function is defined using the following codes:

Show Code
library(zoo)

# Define general function to compute Bayesian Surprise
compute_bayesian_surprise <- function(data, year_col, count_col, window = 5) {
  data <- data %>%
    rename(year = {{year_col}}, count = {{count_col}}) %>%
    complete(year = min(year):max(year), fill = list(count = 0)) %>%
    arrange(year)

  # Calculate moving average (prior belief)
  data <- data %>%
    mutate(prior_mean = rollmean(count, k = window, align = "right", fill = NA))

  # Compute KL Divergence for Poisson distributions
  data <- data %>%
    mutate(
      surprise = ifelse(
        !is.na(prior_mean) & prior_mean > 0 & count > 0,
        count * log(count / prior_mean) - (count - prior_mean),
        NA
      )
    )

  return(data)
}

Bayesian Surprise is computed using KL Divergence between each year’s observed value and a rolling average of the previous five years, treating yearly counts as Poisson-distributed events using the following:

Show Code
# 1. Surprise for Oceanus Folk releases
surprise_releases <- compute_bayesian_surprise(
  oceanus_nodes_by_date,
  release_date,
  oceanus_nodes_count,
  window = 5
)

# 2. Surprise for influenced works
surprise_influence <- compute_bayesian_surprise(
  influence_yearly,
  influence_release_date,
  num_influenced_nodes,
  window = 5
)

# 3. Surprise for artists and contributors
surprise_contributors <- compute_bayesian_surprise(
  creators_by_date,
  influence_release_date,
  people_count,
  window = 5
)

Bayesian Surprise Across Oceanus Folk Influenced Works, Music Releases, and New Influenced Artists

Show Code
plot_data <- bind_rows(
  surprise_releases %>% select(year, surprise) %>% mutate(type = "Music Releases"),
  surprise_influence %>% select(year, surprise) %>% mutate(type = "Influenced Works"),
  surprise_contributors %>% select(year, surprise) %>% mutate(type = "Artists")
) %>%
  filter(!is.na(surprise), year >= 1990)

plot_data <- plot_data %>%
  mutate(type = case_when(
    type == "Music Releases"    ~ "Music Releases",
    type == "Influenced Works"  ~ "Influenced Songs/Albums",
    type == "Artists"           ~ "New Influenced Artists",
    TRUE                        ~ type
  ))

plot_ly(
  data = plot_data, 
  x = ~year, 
  y = ~surprise, 
  color = ~type,
  colors = c(
    "Music Releases"          = "#ADD8E6", 
    "Influenced Songs/Albums" = "#F08080", 
    "New Influenced Artists"  = "#C2E0C6"
  ),
  type = 'scatter', 
  mode = 'lines+markers',
  hoverinfo = "text",
  hovertext = ~paste0(
    "Year: ", year,
    "<br>Category: ", type,
    "<br>Surprise: ", round(surprise, 2)
  )
) %>%
layout(
  legend = list(
    orientation = "h",
    x = 0.5,
    xanchor = "center",
    y = 1.1,
    yanchor = "bottom",
    font = list(size = 12)
  ),
  title = list(
    text = "Bayesian Surprise (Oceanus Folk Influenced Songs/Albums, Music Releases, and New Influenced Artists)",
    x = 0.5,
    xanchor = "center"
  ),
  xaxis = list(title = "Year", dtick = 5, range = c(1990, max(plot_data$year))),
  yaxis = list(title = "Surprise Score (KL Divergence)"),
  margin = list(t = 160, b = 80),
  
     # Add annotations
    annotations = list(
      list(
        x = 2024, 
        y = 100,
        text = "<b>2024: Sailor Shift's Debut</b>",
        xref = "x", yref = "y",
        xanchor = "right",
        showarrow = TRUE,
        arrowhead = 2,
        ax = -30, ay = -40,
        font = list(color = "#2E3192", size = 12)
      ),
      list(
        x = 2028, 
        y = 120,
        text = "<b>2028: Sailor Shift's Breakthrough</b>",
        xref = "x", yref = "y",
        xanchor = "right",
        showarrow = TRUE,
        arrowhead = 2,
        ax = -30, ay = -40,
        font = list(color = "#2E3192", size = 12)
      )
    ),

    # Add vertical dashed lines
    shapes = list(
      list(
        type = "line",
        x0 = 2024, x1 = 2024,
        y0 = 0, y1 = 100,
        line = list(dash = "dash", color = "grey")
      ),
      list(
        type = "line",
        x0 = 2028, x1 = 2028,
        y0 = 0, y1 = 120,
        line = list(dash = "dash", color = "grey")
      )
    )
  )

Insights

The Bayesian Surprise analysis showed that Oceanus Folk’s influence was intermittent rather than a smooth, gradual rise. While cumulative trends in music releases, influenced works and artist numbers may suggest steady growth, Bayesian Surprise uncovers a different insight that there were indeed intermittent surges of activity.

For example, the sharp peaks in surprise scores for artists (red line) in years like 2004, 2010, 2017, and especially 2023 suggest that Oceanus Folk influence had waves of breakthroughs and not slow accumulation.

Influenced works (blue line) increased in 2017, spiked in 2023 and sustained its increase across 2030 to 2033, which shows how Sailor’s debut and breakthrough had catalysed Oceanus Folk genre influence.

Music releases (green line) generally showed lower surprise, which confirms that number of Oceanus Folks music was not a key driver to influence.

Thus, while the overall trend shows long-term growth, Bayesian Surprise confirms that Oceanus Folk’s rise was intermittent. Such bursts also maintained the momentum of the influence for an interval of time.

3.2 Part 2b) What genres and top artists have been most influenced by Oceanus Folk?

3.2.1 What genres have been most influenced by Oceanus Folk?

To determine which genres have been most influenced by Oceanus Folk, all songs and albums were identified. Then, the music (Songs/Albums) that influenced them were obtained to calculate the frequency and percentage of Oceanus Folk’s influence across different musical genre. This analysis reveals the genres that show the strongest impact from Oceanus Folk’s musical style.

Show Code
genre_influence_stats <- creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(infuence_music_collaborate != song_to) %>%
  distinct(song_to, song_genre, infuence_music_collaborate, influence_genre, `Edge Colour`) %>%
  group_by(song_genre) %>%
  summarize(
    total_music = n_distinct(song_to),
    oceanus_influences = n_distinct(na.omit(infuence_music_collaborate[influence_genre == "Oceanus Folk"])),
    percentage_oceanus = round(oceanus_influences / total_music * 100, 1),
  ) %>%
  arrange(desc(oceanus_influences))

genre_influence_stats %>%
  kable(caption = "Genre Ranked by Oceanus Influence") %>%
  kable_styling("striped", full_width = F) %>%
  scroll_box(height = "300px")
Genre Ranked by Oceanus Influence
song_genre total_music oceanus_influences percentage_oceanus
Oceanus Folk 305 97 31.8
Indie Folk 450 93 20.7
Synthwave 382 16 4.2
Doom Metal 348 14 4.0
Dream Pop 742 14 1.9
Synthpop 46 10 21.7
Psychedelic Rock 172 9 5.2
Alternative Rock 258 8 3.1
Indie Rock 208 8 3.8
Desert Rock 125 7 5.6
Americana 184 5 2.7
Blues Rock 109 4 3.7
Southern Gothic Rock 242 4 1.7
Symphonic Metal 64 3 4.7
Avant-Garde Folk 70 2 2.9
Post-Apocalyptic Folk 72 2 2.8
Space Rock 121 2 1.7
Celtic Folk 12 1 8.3
Emo/Pop Punk 77 1 1.3
Indie Pop 134 1 0.7
Jazz Surf Rock 127 1 0.8
Lo-Fi Electronica 136 1 0.7
Acoustic Folk 18 0 0.0
Darkwave 104 0 0.0
Sea Shanties 21 0 0.0
Speed Metal 84 0 0.0
Show Code
library(networkD3)

# Step 1: Prepare data — treat Oceanus Folk as source and include self-influence
sankey_df <- genre_influence_stats %>%
  mutate(
    source = "Oceanus Folk",
    raw_target = ifelse(song_genre == "Oceanus Folk", "Oceanus Folk (in-genre influence)", song_genre),
    value = oceanus_influences,
    target = paste0(raw_target, " (", value, ")")  # ← append the number
  ) %>%
  select(source, target, value) %>%
  arrange(desc(value)) %>%
  head(22)

# Step 2: Create unique nodes
nodes <- data.frame(name = unique(c(sankey_df$source, sankey_df$target)))

# Step 3: Convert names to indices
links <- sankey_df %>%
  mutate(
    source = match(source, nodes$name) - 1,
    target = match(target, nodes$name) - 1
  )

# Step 4: Plot Sankey (Oceanus Folk on the left)
sankeyNetwork(
  Links = links,
  Nodes = nodes,
  Source = "source",
  Target = "target",
  Value = "value",
  NodeID = "name",
  fontSize = 13,
  nodeWidth = 30,
  sinksRight = TRUE  # Flow goes left (source) to right (target)
)
What genres have been most influenced by Oceanus Folk?
  • The table above show the top 10 genres who was most influenced by Oceanus Folk.
  • The Oceanus Folk genre has been the most influenced by itself with about 40% of all music (Songs/Albums) drawing inspiration from Oceanus Folk music.

3.2.2 Which top artists have been most influenced by Oceanus Folk?

To identify the top artists most influenced by Oceanus Folk, all artists (persons or musical groups) who either (a) created Oceanus Folk music (songs/albums) or (b) were influenced by the genre were identified. Then, all music produced by these artists, along with the musical works that influenced their creations, was counted to reveal those who were most influenced.

Show Code
creator_influenced_by_stats <- creator_and_songs_and_influenced_by_creator %>%
  distinct(creator_name, creator_node_type, song_to, song_genre, influenced_by, influenced_by_genre, influenced_by_creator, notable) %>%
  group_by(creator_name, creator_node_type) %>%
  summarize(
    total_music = n_distinct(song_to),
    notable_hits = n_distinct(song_to[notable == TRUE]),
    oceanus_music = n_distinct(song_to[song_genre == "Oceanus Folk"]),
    oceanus_influenced_by = n_distinct(na.omit(influenced_by[influenced_by_genre == "Oceanus Folk" & creator_name != influenced_by_creator])),
    total_oceanus_influence = oceanus_music + oceanus_influenced_by, 
    Percentage_oceanus = round(total_oceanus_influence / total_music * 100, 1), 
  )%>%
  arrange(desc(total_oceanus_influence)) %>%
  filter(creator_node_type == "Person", notable_hits > 10) %>%
  select(-creator_node_type)

creator_influenced_by_stats %>%
  head(10) %>%
  rename(
    `Artist` = creator_name,
    `Total Music` = total_music,
    `Notable Hits` = notable_hits,
    `No. of Oceanus Folk Music` = oceanus_music,
    `Oceanus Folk Influence` = oceanus_influenced_by,
    `Oceanus Folk Music & Influence` = total_oceanus_influence
  ) %>%
  kable(caption = "Ranking of Oceanus Folk Influence on Artists") %>%
  kable_styling("striped", full_width = F) %>%
  scroll_box(height = "200px")
Ranking of Oceanus Folk Influence on Artists
Artist Total Music Notable Hits No. of Oceanus Folk Music Oceanus Folk Influence Oceanus Folk Music & Influence Percentage_oceanus
Sailor Shift 38 25 36 0 36 94.7
Min He 13 13 1 10 11 84.6
Na Ren 13 13 3 7 10 76.9
Qiang Song 12 11 3 5 8 66.7
Min Fu 12 12 0 7 7 58.3
Ping Zeng 11 11 1 6 7 63.6
Xiuying Huang 14 14 2 5 7 50.0
Chao Qiu 12 12 2 4 6 50.0
Chao Wu 13 12 4 2 6 46.2
Yan Qin 13 13 0 6 6 46.2
Show Code
# Step 1: Prepare data — treat Oceanus Folk as source and include self-influence
sankey_df <- creator_influenced_by_stats %>%
  mutate(
    source = "Oceanus Folk",
    raw_target = creator_name,
    value = total_oceanus_influence,
    target = paste0(raw_target, " (", value, ")")  # ← append the number
  ) %>%
  select(source, target, value) %>%
  arrange(desc(value)) %>%
  head(10)

# Step 4: Nodes (no need for group column)
nodes <- data.frame(name = unique(c(sankey_df$source, sankey_df$target)))

# Step 5: Convert names to indices
links <- sankey_df %>%
  mutate(
    source = match(source, nodes$name) - 1,
    target = match(target, nodes$name) - 1
  )

# Step 6: Plot Sankey (same as before)
sankeyNetwork(
  Links = links,
  Nodes = nodes,
  Source = "source",
  Target = "target",
  Value = "value",
  NodeID = "name",
  fontSize = 13,
  nodeWidth = 30,
  sinksRight = TRUE,
  height = 600,
  width = 1000
)
Which top artists have been most influenced by Oceanus Folk?
  • The table above show the top 10 artists who was most influenced by Oceanus Folk
    • These are artists who have produced Oceanus Folk music or have been influenced by it.
    • Top artists are defined by having more than 10 notable hits.
    • These artists are rank by the amount that Oceanus Folk has influenced them.
  • The top artist is Sailor Shift who has produced 36 Oceanus Folk songs.
  • The next artist is Min He, who has produced one Oceanus Folk song and has been influenced by 10 other Oceanus Folk songs, bringing the total to 11 songs.

3.3 Part 2c) From which genres does Oceanus Folk draw most of its contemporary inspiration?

This analysis examines the contemporary influences on Oceanus Folk by using the genre influence patterns methodology similar to Section 3.2.2.

Show Code
###### Calvin, need your help to update the table fig below. The total music should be 305 for OF, similar to first table, but i cant seem to get the number despite using total_music = n_distinct(song_to). Can help to update it? This table dont need % as the logic is how many genre influence OF.####################
  
  
genre_influenced_by_stats <- creator_and_songs_and_influenced_by_creator %>%
  filter(song_genre == "Oceanus Folk") %>%
  distinct(song_to, influenced_by, influenced_by_genre) %>%
  group_by(influenced_by_genre) %>%
  summarize(
    total_music = n_distinct(song_to),
    influenced_by = n_distinct(na.omit(influenced_by)),
    Percentage_oceanus = round(influenced_by / total_music * 100, 1), 
  ) %>%
  arrange(desc(influenced_by))

genre_influenced_by_stats %>%
  head(10) %>%
  rename(
    `Genre` = influenced_by_genre,
    `Total Music` = total_music,
    `Genre influencing Oceanus Folk` = influenced_by
  ) %>%
  kable(caption = "Ranking of Music Genre Influence on Oceanus Folk") %>%
  kable_styling("striped", full_width = F) %>%
  scroll_box(height = "200px")
Ranking of Music Genre Influence on Oceanus Folk
Genre Total Music Genre influencing Oceanus Folk Percentage_oceanus
Oceanus Folk 97 55 56.7
Indie Folk 93 22 23.7
Synthwave 16 17 106.2
Dream Pop 14 15 107.1
Doom Metal 14 13 92.9
Alternative Rock 8 8 100.0
Psychedelic Rock 9 8 88.9
Desert Rock 7 7 100.0
Indie Rock 8 7 87.5
Americana 5 5 100.0

Similar to Section 3.2.2, Sankey diagram is plotted but reversed as we are looking at inward influence to Oceanus Folk.

Show Code
library(networkD3)
library(htmlwidgets)

# Step 1: Prepare data — reverse flow direction: Genre → Oceanus Folk
sankey_df <- genre_influenced_by_stats %>%
  mutate(
    raw_source = influenced_by_genre,
    target = "Oceanus Folk",
    value = influenced_by,
    source = paste0(raw_source, " (", value, ")")
  ) %>%
  select(source, target, value) %>%
  arrange(desc(value)) %>%
  head(22)  # Top 22 genres influencing Oceanus Folk

# Step 2: Create node list
nodes <- data.frame(name = unique(c(sankey_df$source, sankey_df$target)))

# Step 3: Create link list with indices
links <- sankey_df %>%
  mutate(
    source = match(source, nodes$name) - 1,
    target = match(target, nodes$name) - 1
  )

# Step 4: Add tooltip group (Genre → Oceanus Folk)
links$group <- paste0(sankey_df$source, " → ", sankey_df$target, ": ", sankey_df$value)

# Step 5: Render Sankey
p <- sankeyNetwork(
  Links = links,
  Nodes = nodes,
  Source = "source",
  Target = "target",
  Value = "value",
  NodeID = "name",
  fontSize = 13,
  nodeWidth = 30,
  sinksRight = FALSE  # ← Flip layout: sources on right if needed
)

# Step 6: Add tooltips
onRender(p, '
  function(el, x) {
    d3.select(el)
      .selectAll(".link")
      .append("title")
      .text(function(d) { return d.group; });
  }
')
From which genres does Oceanus Folk draw most of its contemporary inspiration?
  • As expected, Oceanus Folk artists primarily draw inspiration from other Oceanus Folk works, demonstrating strong genre continuity.
  • Approximately a fifth of Oceanus Folk compositions appear to be original works without direct influences from existing pieces.
  • Indie Folk emerges as the second most influential genre, accounting for roughly 21% of all Oceanus Folk musical references.

3.4 Part 2d) On the converse, how has Oceanus Folk changed with the rise of Sailor Shift?

We will examine the diversity of musical genres that has influenced and been influenced by Oceanus Folk over time. This can be measured by Genre Entropy, which comes from information theory where higher entropy means more variety and unpredictability, while lower entropy means concentration in fewer genres, as explained by Ferwerda and Schedl (2016) in their research paper.

For our case, we will compute:

Incoming entropy = How diverse other genres were in influencing Oceanus Folk music in a given year (i.e. what genres inspired new Oceanus Folk music)

Outgoing entropy = How diverse the genres were which Oceanus Folk had influenced (i.e. what range of genres borrowed from Oceanus Folk).

By plotting both directions of entropy over time using a mirrored bar chart and mapping it against Sailor’s debut and breakthrough, we can visualise the change and impact clearly.

Show Code
# Step 1: Filter influence edges
influence_edges <- mc1_edges_clean %>%
  filter(`Edge Type` %in% c("InStyleOf", "CoverOf", "InterpolatesFrom", 
                            "LyricalReferenceTo", "DirectlySamples"))

# Step 2: Join genre and release date info
influence_genres <- influence_edges %>%
  left_join(mc1_nodes_clean %>% select(name, genre), by = c("from" = "name")) %>%
  rename(source_genre = genre) %>%
  left_join(mc1_nodes_clean %>% select(name, genre, release_date), by = c("to" = "name")) %>%
  rename(target_genre = genre, to_release = release_date) %>%
  left_join(mc1_nodes_clean %>% select(name, release_date), by = c("from" = "name")) %>%
  rename(from_release = release_date)

# Step 3a: Incoming entropy (genres influencing Oceanus Folk)
incoming_entropy_yearly <- influence_genres %>%
  filter(target_genre == "Oceanus Folk", !is.na(source_genre), !is.na(to_release)) %>%
  mutate(year = as.integer(to_release)) %>%
  group_by(year, source_genre) %>%
  summarise(count = n(), .groups = "drop") %>%
  group_by(year) %>%
  mutate(p = count / sum(count)) %>%
  summarise(entropy = entropy::entropy(p, unit = "log2")) %>%
  mutate(direction = "Incoming")

# Step 3b: Outgoing entropy (genres that Oceanus Folk influenced)
outgoing_entropy_yearly <- influence_genres %>%
  filter(source_genre == "Oceanus Folk", !is.na(target_genre), !is.na(from_release)) %>%
  mutate(year = as.integer(from_release)) %>%
  group_by(year, target_genre) %>%
  summarise(count = n(), .groups = "drop") %>%
  group_by(year) %>%
  mutate(p = count / sum(count)) %>%
  summarise(entropy = entropy::entropy(p, unit = "log2")) %>%
  mutate(direction = "Outgoing")

# Step 4: Combine and flip outgoing for mirrored effect
entropy_yearly <- bind_rows(
  incoming_entropy_yearly,
  outgoing_entropy_yearly %>% mutate(entropy = -entropy)
)

# Step 5: Plot mirrored entropy over time with hover text
entropy_plot <- ggplot(entropy_yearly, aes(
  x = year, y = entropy, fill = direction,
  text = paste0(
    "Year: ", year,
    "\nDirection: ", direction,
    "\nEntropy: ", round(abs(entropy), 3), " bits"
  )
)) +
  geom_col(width = 0.8) +
  geom_hline(yintercept = 0, color = "black") +
  scale_fill_manual(
    name = "Entropy Direction",
    values = c("Incoming" = "lightblue", "Outgoing" = "darkblue")
  ) +
  scale_y_continuous(
    breaks = seq(-4, 4, by = 1),
    labels = abs(seq(-4, 4, by = 1)),
    limits = c(-4, 4)
  ) +
  labs(
    title = "Genre Entropy of Oceanus Folk Over Time",
    x = "Year",
    y = "Genre Entropy (bits)"
  ) +
  theme_minimal() +
  theme(legend.position = "bottom")

# Add annotations after setting `max_entropy_val`
max_entropy_val <- max(abs(entropy_yearly$entropy), na.rm = TRUE)

entropy_plot <- entropy_plot +
  geom_vline(xintercept = 2024, linetype = "dashed", color = "grey50", linewidth = 0.7) +
  geom_vline(xintercept = 2028, linetype = "dashed", color = "grey50", linewidth = 0.7) +
  annotate("text", x = 2024.5, y = max_entropy_val + 0.4,
           label = "2024: Sailor Shift's Debut",
           color = "#2E3192", fontface = "bold", size = 3.5, hjust = 1) +
  annotate("text", x = 2028.5, y = max_entropy_val + 0.8,
           label = "2028: Sailor Shift's Breakthrough",
           color = "#2E3192", fontface = "bold", size = 3.5, hjust = 1) +
  annotate("segment", x = 2024.5, y = max_entropy_val + 0.4,
           xend = 2024, yend = max_entropy_val - 0.2,
           arrow = arrow(length = unit(0.2, "cm")), color = "grey50") +
  annotate("segment", x = 2028.5, y = max_entropy_val + 0.8,
           xend = 2028, yend = max_entropy_val - 0.2,
           arrow = arrow(length = unit(0.2, "cm")), color = "grey50")

# Use plotly for interactivity
ggplotly(entropy_plot, tooltip = "text") %>%
  layout(legend = list(orientation = "h", x = 0.5, xanchor = "center", y = -0.2))

Insights

Before 2013: Limited Influence during earlier years

During the early years of Oceanus Folk, both incoming and outgoing entropy remained sparse, indicating limited cross-genre interaction. However, there are more bars for incoming entropy, showing that Oceanus Folk was more often influenced by other genres than influencing others. This suggests that during the earlier years, Oceanus Folk was still developing its identity by absorbing influence from a diverse set of genres.

2013 to 2024: Influence Growth

Between 2013 and 2024, we observe a gradual and sustained rise in both incoming and outgoing entropy.This period can be seen as the maturation phase of Oceanus Folk, where it begins to both learn from and contribute to the wider music landscape.

2024 onwards: Sailor Shift’s Rise

Incoming entropy began to decline, suggesting that Oceanus Folk was no longer absorbing influence from other genres as it solidified its musical identity.

In contrast, outgoing entropy peaked and remained elevated, reflecting that other genres increasingly drew inspiration from Oceanus Folk. This sustained high outgoing influence signals that Oceanus Folk had become a genre of reference across many genres.

4 Part 3 Oceanus Folks’s Rising Stars

We will use visualizations to develop a profile of what it means to be a rising star in the music industry.

4.1 Analysing the careers of all Artists

The analysis identifies all musical artists, including both the songs/albums they produced and their influence relationships—tracking both the musical works they have influenced and the artists they have impacted throughout their careers.

A grading rubric is added to assess artists based on four equally weighted categories:

  1. Number of Music Releases
  2. Number of Notable Music
  3. Number of New Artists Influenced and Collaborated With
  4. Number of Music Influenced

Each category’s score will be normalised using the formula:

Show Code
library(latex2exp)
plot(1, 1, type = "n", ann = FALSE, axes = FALSE)
text(1, 1, 
     latex2exp::TeX("$\\frac{Artist's\\ Score - min((Score))}{max((Score)) - min((Score))}$"), 
     cex = 2)

The Star Factor will aggregate these normalized scores (maximum: 4) to rank the artists.

Show Code
# Data Preparation
  
# Step 1: To highlight songs that the creator influence that is not produced by same creator
creator_influence_lists <- creator_and_songs_and_influences_and_creators_collaborate %>%
  group_by(creator_name, creator_node_type, song_to, song_name, creator_release_date, song_genre, notable) %>%
  distinct () %>%
  summarize(
    unique_collaborate = list(unique(na.omit(infuence_music_collaborate[creator_from != influence_creator & `Edge Colour` == "Creator Of"]))),
    unique_influence_creators = list(unique(na.omit(influence_creator[creator_from != influence_creator & `Edge Colour` == "Influenced By"]))),
    unique_influence_music = list(unique(na.omit(infuence_music_collaborate[creator_from != influence_creator & !is.na(influence_genre)])))
  )

# Step 2: Aggregate unique influences per creator
creator_stats <- creator_influence_lists %>%
  group_by(creator_name) %>%
  summarize(
    total_songs = n_distinct(song_to),
    notable_hits = sum(notable == TRUE, na.rm = TRUE),
    collaboration = length(unique(unlist(unique_collaborate))),
    influence_creators = length(unique(unlist(unique_influence_creators))),
    collaboration_influence_creator = length(unique(c(unlist(unique_influence_creators),unlist( unique_collaborate)))),
    influence_music = length(unique(unlist(unique_influence_music)))
  )

# Step 3: Create Scoring Rubric
creator_rankings <- creator_stats %>%
  mutate(
    # Normalize each metric (0-1 scale)
    songs_score = (total_songs - min(total_songs)) / (max(total_songs) - min(total_songs)),
    notable_score = (notable_hits - min(notable_hits)) / (max(notable_hits) - min(notable_hits)),
    artists_score = (collaboration_influence_creator - min(collaboration_influence_creator)) / (max(collaboration_influence_creator) - min(collaboration_influence_creator)),
    music_score = (influence_music - min(influence_music)) / (max(influence_music) - min(influence_music)),
    
    # Calculate composite score (equal weighting)
    composite_score = songs_score + notable_score + artists_score + music_score
  ) %>%
  arrange(desc(composite_score)) %>%
  select(creator_name, total_songs, notable_hits, collaboration_influence_creator, influence_music, composite_score)

# Step 4: Final Ranked Table
creator_rankings %>%
  mutate(
    Rank = row_number(),
    `Star Factor` = round(composite_score, 2)
  ) %>%
  select(Rank, creator_name, total_songs, notable_hits, collaboration_influence_creator, 
         influence_music, `Star Factor`) %>%
  rename(
    `Artist` = creator_name,
    `Total Music` = total_songs,
    `Notable Hits` = notable_hits,
    `Artist Influ & Colab` = collaboration_influence_creator,
    `Music Influenced` = influence_music
  ) %>%
  kable(caption = "Artists Ranked by Star Factor") %>%
  kable_styling("striped", full_width = F) %>%
  scroll_box(height = "300px")
Artists Ranked by Star Factor
Rank Artist Total Music Notable Hits Artist Influ & Colab Music Influenced Star Factor
1 Min Fu 12 12 598 162 2.64
2 Jay Walters 37 35 51 50 2.37
3 Jing Kang 29 28 176 70 2.28
4 Min Tao 6 6 588 157 2.26
5 Chao Tan 5 5 592 155 2.20
6 Ping Tian 32 31 115 42 2.18
7 Juan Gao 5 5 572 147 2.11
8 Qiang Yuan 9 9 511 125 2.10
9 Kimberly Snyder 29 27 153 49 2.09
10 Ping Meng 24 23 175 83 2.08
11 Yang Wan 24 23 274 56 2.08
12 Brett Brown 4 4 540 143 1.98
13 Angelika Osojca 28 26 95 55 1.97
14 Guiying Lu 3 3 554 142 1.94
15 Sailor Shift 38 25 48 0 1.79
16 Brittany Meyers 1 1 528 141 1.78
17 Corey Moody 1 1 528 141 1.78
18 Kristina Willis 1 1 528 141 1.78
19 Lisa Evans 1 1 528 141 1.78
20 Peter Huff 1 1 528 141 1.78
21 Susan Richardson 1 1 528 141 1.78
22 Thomas Kelly 1 1 528 141 1.78
23 Gang Chen 11 11 392 84 1.76
24 Ante Reinhardt-Hande 4 4 483 116 1.72
25 Jing Cui 22 22 193 26 1.68
26 Szymon Pyć 26 26 45 29 1.67
27 Guiying Cai 3 3 480 116 1.66
28 Yang Zhao 20 20 216 33 1.65
29 Tao Yang 20 19 184 40 1.61
30 Fang Zou 17 17 198 52 1.57
31 Megan Bennett 21 21 54 49 1.53
32 Andrew Williams 23 23 38 35 1.53
33 Maik Mude 19 19 77 53 1.49
34 Catherine Clay 22 21 52 34 1.46
35 Matthew Hancock 21 21 58 36 1.46
36 Na Ren 13 13 255 54 1.46
37 Qiang Tang 20 19 151 17 1.41
38 Min Lei 20 20 133 15 1.40
39 Yong Zheng 22 20 47 25 1.37
40 Bekir Bruder 14 14 187 46 1.35
41 Chao Wu 13 12 235 46 1.34
42 Lei Yuan 16 16 149 30 1.30
43 Yang Peng 15 14 138 43 1.27
44 Guiying Bai 19 18 64 25 1.26
45 Deborah Lucas 15 15 91 45 1.24
46 Urszula Stochmal 18 18 37 32 1.23
47 Xiuying Han 4 4 328 75 1.21
48 Xiuying Fan 12 12 192 37 1.19
49 Gerald Mullins 16 14 112 31 1.18
50 Brian Wright 15 15 50 46 1.17
51 Ming Huang 9 9 241 48 1.17
52 Ming Sun 10 10 134 66 1.16
53 Corinna Gute 11 11 180 44 1.16
54 Xia Wu 15 13 111 35 1.15
55 Jing Wang 13 13 147 32 1.14
56 Leyla Graf-Gotthard 17 16 87 16 1.13
57 Lei Duan 17 16 95 13 1.13
58 Min Kong 15 14 121 23 1.12
59 Quattro Voci 16 16 31 33 1.12
60 Guiying Liao 16 16 88 16 1.11
61 Li Lu 13 12 86 47 1.10
62 Jun Hao 11 11 175 36 1.10
63 Xiuying Yao 13 12 139 31 1.09
64 Gang Cui 7 7 228 55 1.08
65 Guiying Qin 9 9 198 45 1.08
66 Jing Tian 12 12 161 26 1.07
67 Xia Jia 9 8 199 45 1.06
68 Gang Zhao 16 16 49 16 1.04
69 Belinda Knappe 13 13 110 26 1.04
70 Regina Hesse 13 13 117 23 1.03
71 Wei Shen 7 7 219 49 1.03
72 Guiying Shi 15 15 76 15 1.03
73 Tao Gao 16 16 68 7 1.02
74 Joshua Herring 14 12 91 28 1.02
75 Xiuying Huang 14 14 91 18 1.01
76 Min He 13 13 119 18 1.01
77 Xiulan Ren 9 9 120 53 1.00
78 Daniël Reijers 14 13 46 32 1.00
79 Li Lei 11 11 136 30 1.00
80 Ping Zhong 12 12 127 23 0.99
81 Mareen Schacht 13 13 61 31 0.99
82 Yong Fu 12 12 123 21 0.98
83 Yan Tang 11 11 139 25 0.97
84 Yong Dong 14 14 85 12 0.97
85 Ming Jiang 8 8 192 37 0.97
86 Jie Yan 13 12 89 24 0.96
87 Xia Du 14 14 79 13 0.96
88 Jun Han 10 10 148 26 0.94
89 Lei Liao 12 11 76 32 0.94
90 Qiang Lei 8 8 96 57 0.93
91 Jun Dai 11 11 128 21 0.93
92 Ping He 13 13 91 13 0.93
93 Wei Liao 10 10 146 24 0.92
94 Juan Yin 12 11 112 19 0.92
95 Lei Yan 12 12 83 22 0.91
96 Juan Li 12 12 82 22 0.91
97 Xiuying Yin 12 12 105 15 0.91
98 Jun Yu 12 12 112 13 0.91
99 Marcus Spence 13 12 43 26 0.90
100 Ping Luo 14 14 56 8 0.89
101 Jie Lai 12 12 94 15 0.89
102 Gang Bai 13 12 81 14 0.89
103 Zacharie Martins 8 8 152 34 0.88
104 Sonic Renegade 12 10 82 26 0.88
105 Ping Bai 9 9 102 38 0.88
106 Jun Zhou 8 7 163 35 0.88
107 Shaan Sanghvi 13 12 55 19 0.88
108 Qiang Song 12 11 99 16 0.88
109 Yan Qin 13 13 63 12 0.88
110 Eugenia Mitschke 12 12 48 25 0.87
111 Chelsea Martinez 13 13 45 16 0.87
112 Jun Xia 11 11 102 18 0.87
113 Xia Zeng 12 12 90 12 0.86
114 Li Yang 12 12 92 11 0.86
115 Qiang Dong 6 6 186 39 0.86
116 Lei Xue 13 13 67 8 0.86
117 Yuta Tanaka 11 11 61 27 0.85
118 Juan Zhong 10 10 123 19 0.85
119 Arctic Scholars 9 8 73 46 0.85
120 Jun Yin 12 12 73 14 0.85
121 Lei Yi 11 11 113 12 0.85
122 Yan Zou 11 11 94 17 0.85
123 Guiying Kang 8 8 140 31 0.84
124 Lei Fu 10 10 121 18 0.84
125 Jun Mao 8 8 149 28 0.84
126 Chao Qiu 12 12 84 9 0.84
127 Ming Long 11 11 76 20 0.84
128 Li Zheng 12 11 63 18 0.83
129 Qiang Fang 9 9 122 24 0.83
130 Filippo Pelli 11 10 91 19 0.83
131 Guiying Pan 11 11 93 13 0.82
132 Li Luo 12 12 47 16 0.82
133 Juan Feng 9 7 134 27 0.81
134 Georges Besnard 11 11 44 24 0.81
135 Tao Sun 11 11 81 13 0.80
136 Jun Zou 11 10 50 25 0.79
137 Yan Feng 9 9 123 18 0.79
138 John Anderson 8 7 114 34 0.79
139 Alisha Fuller 5 5 169 41 0.79
140 Tao Dai 12 12 58 8 0.79
141 Peter Haering 11 11 70 13 0.78
142 Qiang Zhao 11 11 66 14 0.78
143 Fang Yin 12 12 43 9 0.77
144 Jun Fang 12 11 67 7 0.77
145 Kelly Stewart 10 8 79 26 0.76
146 Guiying Gao 11 11 59 13 0.76
147 Kimberly Castillo 11 9 71 19 0.76
148 Rachel Jackson 8 8 103 28 0.76
149 Yan Jin 10 9 68 24 0.76
150 Robin Webster 10 10 43 26 0.76
151 Jun Meng 9 9 107 17 0.76
152 Susan Edwards 3 3 194 46 0.75
153 Min Qin 11 11 65 8 0.74
154 Qiang Gao 9 9 98 16 0.74
155 Min Gao 7 7 146 21 0.74
156 José Berger 4 4 163 43 0.73
157 Adam Sanders 1 1 240 49 0.73
158 Christina Blair 1 1 240 49 0.73
159 Gabriela Campos 1 1 240 49 0.73
160 Noah Wilson 1 1 240 49 0.73
161 Tyler Brown 1 1 240 49 0.73
162 Min Zhao 9 9 99 15 0.73
163 Yang Pan 9 8 105 18 0.73
164 Jun Shao 8 8 105 22 0.73
165 Samantha Bullock 8 8 71 31 0.73
166 Tao Yao 10 10 82 10 0.73
167 Wei Ding 10 9 47 24 0.73
168 Ping Zeng 11 11 52 9 0.73
169 Min Cao 7 7 118 27 0.73
170 Chao Yao 7 7 92 34 0.73
171 Guiying Zhou 11 11 62 6 0.73
172 Gang Peng 6 6 139 30 0.72
173 Brüderklang 2 2 212 46 0.72
174 Tao Peng 10 9 73 16 0.72
175 Yan Liu 8 8 118 17 0.72
176 Navya Sastry 11 10 45 14 0.72
177 Jun Chang 7 7 118 25 0.71
178 Li Gu 10 10 64 12 0.71
179 Na Dong 10 10 69 10 0.71
180 Gang Shen 11 11 50 6 0.71
181 Lei Wang 11 11 33 10 0.70
182 Chao Zhu 7 7 110 25 0.70
183 Na Qian 9 9 80 15 0.70
184 Ming Duan 6 6 119 31 0.70
185 Ming Zhou 9 9 81 14 0.70
186 Gang Wan 6 6 136 26 0.69
187 Yan Kang 11 11 36 8 0.69
188 Li Song 7 6 130 23 0.69
189 Marilyn Moore 2 2 194 46 0.69
190 Qiang He 9 9 74 15 0.69
191 Shannon Gordon 9 7 81 21 0.68
192 Tao Ren 6 6 139 23 0.68
193 Yong Shen 7 6 115 25 0.68
194 Chao Zhong 10 10 56 9 0.68
195 Ping Liao 10 10 56 9 0.68
196 Li Jin 10 10 67 6 0.68
197 Yan Yan 10 10 63 7 0.68
198 Xia Dai 10 10 51 10 0.68
199 Yan Cai 10 10 51 10 0.68
200 Sandra Smith 3 2 179 43 0.68
201 Yong Wei 5 5 147 29 0.68
202 Xiulan Ye 3 2 180 42 0.67
203 Ming Xiang 9 9 74 12 0.67
204 Ming Chen 8 7 109 16 0.67
205 Gang Shao 7 7 108 20 0.67
206 Wei Zhao 8 8 67 22 0.67
207 Lupe Miró 9 9 77 10 0.66
208 Na Lai 9 9 77 10 0.66
209 Wayfinder's Lament 18 7 2 0 0.66
210 Tao Cui 9 9 80 9 0.66
211 Fang Su 9 9 75 10 0.66
212 Yan Liang 9 9 75 10 0.66
213 Xiuying Yuan 7 7 108 19 0.66
214 The Salty Wakes 21 4 3 0 0.66
215 Jie Cui 9 9 70 11 0.66
216 Theresa Rivera 8 8 92 14 0.66
217 Yang Cui 9 9 73 10 0.66
218 Ming Lei 5 5 134 29 0.65
219 Gianfranco Falier 8 8 71 19 0.65
220 Sean Jones 9 8 47 21 0.65
221 Gang Qin 9 9 62 12 0.65
222 Juan Fan 8 6 103 19 0.65
223 Lei Bai 8 8 83 15 0.65
224 Xiuying Xiang 9 9 64 11 0.65
225 Bradley Hayes 8 8 49 24 0.65
226 Dennis Lee 8 8 49 24 0.65
227 Rüdiger Graf 10 9 48 10 0.64
228 Daniel Lyons 10 10 41 7 0.64
229 Xiulan Yi 9 9 59 11 0.64
230 Xiulan Shen 9 9 66 9 0.64
231 Lei Tan 6 6 121 21 0.64
232 Jadwiga Gertz 7 7 91 20 0.64
233 Lei Jin 8 7 91 15 0.63
234 Guiying Yan 10 9 54 7 0.63
235 Wei Mo 10 10 43 5 0.63
236 Fang Tao 8 8 91 10 0.63
237 Li Xie 6 6 113 22 0.63
238 Xia Ren 9 9 56 10 0.63
239 Ewelina Jeszke 8 8 40 23 0.63
240 Ping Sun 9 9 54 10 0.63
241 Angelina Dallapé-Brunelleschi 10 10 17 11 0.63
242 Michelotto Comisso 10 10 17 11 0.63
243 Jie Tao 10 10 42 4 0.62
244 Ping Ding 5 5 123 27 0.62
245 Miki Hayashi 8 8 81 11 0.62
246 Fang Yan 3 3 161 34 0.62
247 Min Liao 8 8 83 10 0.62
248 Xiuying Zeng 6 6 119 18 0.62
249 Clémence Vincent 3 3 166 32 0.61
250 Laura Turner 7 7 73 21 0.61
251 Jun Jia 5 5 121 26 0.61
252 Min Jin 9 9 50 9 0.61
253 Chad Hampton 10 10 19 8 0.61
254 Brian Johnson 6 6 110 19 0.61
255 Emily Nelson 8 8 65 13 0.61
256 Xiulan Zeng 10 9 34 8 0.61
257 Guiying Dai 6 6 109 19 0.61
258 Alla Lorch 10 10 16 8 0.61
259 Sandro Gröttner 10 10 16 8 0.61
260 Qiang Lu 9 9 56 6 0.60
261 Vanessa Ramos 7 7 88 15 0.60
262 Cristian Bonilla 9 9 39 10 0.60
263 Juan Shi 8 8 72 10 0.60
264 Tao Ye 7 7 79 17 0.60
265 Ping Mo 8 8 75 9 0.60
266 Xia Cui 8 8 63 12 0.60
267 Ming Qiao 7 7 81 16 0.60
268 Xiulan Xue 9 9 58 4 0.60
269 Joseph Vargas 1 1 209 35 0.59
270 Keith Harrison 1 1 209 35 0.59
271 Donna Caldwell 2 1 170 41 0.59
272 Juan Jia 7 7 85 14 0.59
273 Na Zhou 10 9 27 7 0.59
274 Xiuying Ding 10 8 39 8 0.59
275 Yan Long 9 9 36 8 0.58
276 Vidur Kapadia 9 9 16 13 0.58
277 SideShift 6 6 97 18 0.58
278 Yang Ye 7 7 82 13 0.58
279 Min Qian 8 8 67 8 0.58
280 Cindy Lübs 7 7 74 15 0.58
281 Chao Zeng 7 7 77 14 0.58
282 Xiuying Song 8 8 58 10 0.58
283 Na Shao 8 8 69 7 0.58
284 Fang Xiong 4 4 128 27 0.58
285 Ida Hentschel 6 6 87 20 0.58
286 Gang Fan 7 7 90 10 0.57
287 Thibault Potier 7 7 53 20 0.57
288 Jie Fan 7 6 88 15 0.57
289 Min Deng 8 8 56 10 0.57
290 Radical Syntax 7 7 66 16 0.57
291 Jie Huang 4 4 121 28 0.57
292 Yan Shi 9 9 39 5 0.57
293 Wei Gao 7 7 72 14 0.57
294 Li Duan 4 4 120 28 0.57
295 Yang Chang 6 6 94 17 0.57
296 Yong Meng 5 5 94 26 0.57
297 Tao Xie 8 8 51 10 0.56
298 Michael Boyd 8 7 31 20 0.56
299 Lei Gong 7 7 69 14 0.56
300 Wei Xiong 8 8 65 6 0.56
301 Gang Wen 9 8 48 6 0.56
302 Cynthia Dixon 8 8 53 9 0.56
303 Chao Yi 7 7 81 10 0.56
304 Xiuying Tang 8 8 66 5 0.56
305 Jun Jiang 7 7 84 9 0.56
306 Min Duan 9 7 62 6 0.56
307 Yan Lin 8 8 57 7 0.56
308 Roy Tschentscher 7 7 57 16 0.56
309 Tao Lei 8 8 56 7 0.55
310 Yong Lin 8 6 82 9 0.55
311 Li Ma 8 8 51 8 0.55
312 Na Fan 8 8 51 8 0.55
313 Juan Pan 6 6 91 15 0.55
314 Nordic Kitchen Collective 7 7 65 13 0.55
315 Keith Larsen 3 3 124 33 0.55
316 Guiying Ren 8 8 52 7 0.55
317 Lucrezia Zamengo 9 9 15 8 0.55
318 Min Yu 6 4 112 18 0.55
319 Li Wan 8 8 59 5 0.55
320 Jun Cao 8 8 44 9 0.55
321 Xiuying Zhu 4 4 114 26 0.55
322 Bobby Tucker 2 2 162 31 0.55
323 Christopher Reese 2 2 162 31 0.55
324 Daniel Lee 2 2 162 31 0.55
325 Leon Hutchinson 2 2 162 31 0.55
326 Melanie Bautista 2 2 162 31 0.55
327 Rémy-Bertrand Bailly 2 2 162 31 0.55
328 Xiuying Dai 6 6 88 15 0.55
329 Li Lin 8 8 51 7 0.55
330 Ochre 9 9 16 7 0.54
331 Ekkehard Förster 9 9 12 8 0.54
332 Wei Yuan 7 7 60 13 0.54
333 Jun Ye 7 7 63 12 0.54
334 Xiulan Lu 9 9 37 1 0.54
335 Kashvi Dhillon 8 7 39 14 0.54
336 Tao Zhang 8 8 47 7 0.54
337 Juan Wu 8 8 50 6 0.54
338 Xiuying Li 7 7 68 10 0.54
339 Kathryn Golden 7 6 74 13 0.54
340 Fang Zhong 7 6 70 14 0.54
341 Fang Lin 6 6 82 15 0.54
342 Yang Tang 8 8 55 4 0.53
343 Xiuying Zhang 5 5 99 19 0.53
344 Jing Jin 9 7 44 7 0.53
345 Li Bai 6 6 91 12 0.53
346 Qiang Shen 2 2 146 33 0.53
347 Chao Tian 7 7 57 12 0.53
348 Zenith Horizon 6 6 49 23 0.53
349 Xiulan Gong 7 7 67 9 0.53
350 Xiuying Xie 5 5 89 21 0.53
351 Na Wen 7 7 63 10 0.53
352 Florian van Santen-Sanders 8 8 44 6 0.53
353 Na Peng 8 7 63 5 0.53
354 Ping Fu 3 3 127 28 0.52
355 Chao Wan 8 8 38 7 0.52
356 Tao Xu 6 6 86 12 0.52
357 Xiulan Tao 7 7 60 10 0.52
358 Yan Yin 5 5 56 29 0.52
359 Xiulan Zhao 8 8 44 5 0.52
360 Yan Tian 6 6 77 14 0.52
361 Jun Lin 5 5 88 20 0.52
362 Xia Li 8 8 51 3 0.52
363 Thomas Weber 4 4 88 29 0.52
364 David Wilcox 8 7 26 14 0.52
365 John Ferguson 3 3 116 30 0.52
366 Ismet Heser 7 6 22 24 0.52
367 Ming Ding 8 8 45 4 0.52
368 Xiulan Liao 5 5 92 18 0.52
369 Jie Zheng 8 8 40 5 0.52
370 Yan Xiong 6 6 84 11 0.51
371 Przemysław Grupa 4 4 58 36 0.51
372 Na Jia 6 6 80 12 0.51
373 Ming Xia 3 3 128 26 0.51
374 Yan Wen 8 8 35 6 0.51
375 Qiang Lai 6 6 46 21 0.51
376 Aimée van Dijk 7 7 49 11 0.51
377 Hans Josef Haering 2 2 134 33 0.51
378 Juan Fu 7 7 56 9 0.51
379 Min Long 7 7 59 8 0.51
380 Na Pan 7 7 59 8 0.51
381 Christopher Smith 8 8 22 9 0.51
382 Na Zhong 8 8 33 6 0.51
383 Adam Davis 5 5 40 31 0.51
384 Yong Lei 7 7 62 7 0.51
385 Constance Guibert 3 3 117 28 0.51
386 Ignazio Pastine 3 3 117 28 0.51
387 Tao Zeng 7 7 54 9 0.51
388 Guiying Song 4 4 101 23 0.51
389 Jun Zhang 6 6 71 13 0.51
390 Tonya Brown 6 6 57 16 0.50
391 Wei Wan 7 6 69 8 0.50
392 Allison Patterson 5 4 80 23 0.50
393 Yan Xue 5 5 85 17 0.50
394 Samuel Rogers 6 6 69 12 0.50
395 Jun Xu 3 3 113 27 0.50
396 Tao Jin 7 7 50 8 0.50
397 Fang Wu 6 6 72 11 0.49
398 Xiulan Sun 8 8 38 2 0.49
399 Fang Bai 6 5 44 23 0.49
400 Yang Zhang 6 6 71 11 0.49
401 Xiuying Cheng 7 7 45 9 0.49
402 Andrew Green 1 1 162 31 0.49
403 Eric Bailey 1 1 162 31 0.49
404 Inès Lebreton 1 1 162 31 0.49
405 Lei Zhou 7 7 51 7 0.49
406 Jana Lindner 8 8 14 8 0.49
407 Juan Qiao 6 6 43 18 0.49
408 Li Liao 7 6 56 10 0.49
409 Qiang Shi 7 7 61 4 0.49
410 Xiulan Li 6 6 68 11 0.49
411 Qiang Bai 8 8 31 3 0.49
412 Xiulan Shi 7 7 31 12 0.49
413 Isabella Farinelli 6 5 73 14 0.49
414 Xiulan Liang 7 7 48 7 0.49
415 Yan Han 5 5 76 17 0.48
416 Min Hu 6 6 61 12 0.48
417 Yong Tao 6 6 72 9 0.48
418 Burrowing Rodent 5 5 68 19 0.48
419 Michelangelo Nolcini 5 5 68 19 0.48
420 Rita Iannucci 5 5 68 19 0.48
421 Jing Zhang 8 7 37 5 0.48
422 Min Jia 8 7 37 5 0.48
423 Xiulan Yao 6 6 64 11 0.48
424 Jun Wan 8 8 27 3 0.48
425 Yan Su 7 7 49 6 0.48
426 Chao Qiao 6 6 60 12 0.48
427 Guiying Ding 5 5 89 13 0.48
428 Na Deng 8 7 39 4 0.48
429 Lei Shen 6 6 57 12 0.48
430 Gang He 8 8 31 1 0.48
431 Karen Wright 7 7 27 11 0.48
432 Li Long 6 6 52 13 0.47
433 Jie Jiang 8 7 43 2 0.47
434 Jun Qiao 7 7 48 5 0.47
435 Jun Liang 4 4 99 18 0.47
436 Tao Liao 6 6 69 8 0.47
437 Xiuying Dong 7 7 43 6 0.47
438 Jing Pan 6 5 71 12 0.47
439 Nathan Bryant 5 5 76 15 0.47
440 Xia Lin 8 7 32 4 0.47
441 Yong Yao 7 7 37 7 0.47
442 Xiulan Qin 7 7 48 4 0.47
443 Tao Bai 7 6 54 7 0.47
444 Yong Wen 6 6 70 7 0.47
445 Na Ma 6 6 55 11 0.47
446 Dora Säuberlich 1 1 140 33 0.47
447 Jun Duan 5 5 77 14 0.47
448 Jonathan Poole 7 7 25 10 0.47
449 Interval Shift 7 7 36 7 0.47
450 Martino Michelangeli 7 7 36 7 0.47
451 Jing Guo 7 7 47 4 0.47
452 Xiulan Hou 6 6 56 10 0.46
453 Ping Xia 7 7 30 8 0.46
454 John Ramirez 4 3 58 32 0.46
455 Li Gong 8 8 26 0 0.46
456 Jie Shao 4 4 92 18 0.46
457 Erdogan Baum 5 5 73 14 0.46
458 Gang Zheng 5 5 73 14 0.46
459 Borys Kościk 8 8 10 4 0.46
460 Brandon Bartlett 5 5 69 15 0.46
461 Lunar Syntax 5 5 69 15 0.46
462 Na Guo 7 7 43 4 0.46
463 Jun Yi 6 6 54 10 0.46
464 The Wandering Pulse 7 7 17 11 0.46
465 Qiang Yang 6 6 65 7 0.46
466 Jie Wen 5 5 65 16 0.46
467 Tarini Hans 7 6 34 11 0.46
468 Ping Cai 5 5 76 13 0.46
469 Ping Pan 4 4 87 19 0.46
470 Wei Cai 4 4 94 17 0.46
471 Fang Duan 6 6 57 9 0.46
472 Lei Qiu 6 5 63 12 0.46
473 Qiang Cui 7 7 42 4 0.46
474 Xiulan He 6 6 53 10 0.46
475 Nordic Echo 6 5 33 20 0.46
476 Min Ye 6 6 37 14 0.45
477 Lei Hu 6 6 59 8 0.45
478 Juan Du 4 4 81 20 0.45
479 Katrin Steckel 1 1 132 33 0.45
480 Roswita Hande 1 1 132 33 0.45
481 Kaitlyn Warren 5 5 39 22 0.45
482 Linda Reed 8 7 15 6 0.45
483 Ming Cui 6 6 64 6 0.45
484 Guiying Hou 7 7 38 4 0.45
485 Gang Yuan 7 7 34 5 0.45
486 Min Xiang 6 6 67 5 0.45
487 Xia Xiong 6 6 55 8 0.45
488 Guiying Zeng 7 7 40 3 0.45
489 Drowned Harbor 12 5 4 0 0.45
490 Liliana Heidrich 4 4 106 12 0.45
491 Xiuying Meng 7 5 55 8 0.45
492 Wei Tang 8 7 19 4 0.45
493 Laetitia Bertrand-Becker 7 7 24 7 0.45
494 Jun Guo 6 6 57 7 0.45
495 Gang Yang 7 6 48 5 0.44
496 Zachary Harris 7 6 33 9 0.44
497 Yong Su 7 6 44 6 0.44
498 Jie Xu 6 5 77 6 0.44
499 Tao Yin 8 7 25 2 0.44
500 Lei Tang 8 6 34 4 0.44
501 Juan Duan 5 5 70 12 0.44
502 Ming Kang 4 4 70 21 0.44
503 Juan Kang 7 7 33 4 0.44
504 Juan Yao 7 6 50 4 0.44
505 Mia Carsten 5 5 66 13 0.44
506 Nathan Perez 5 5 69 12 0.44
507 Bryan Smith 6 6 50 8 0.44
508 Xiuying Kong 4 4 109 10 0.44
509 Lei He 7 7 35 3 0.44
510 Ping Lai 3 3 105 20 0.44
511 Na Cui 5 4 74 15 0.44
512 Yang Yu 7 7 31 4 0.44
513 Min Li 7 6 48 4 0.44
514 Na Su 5 5 75 10 0.44
515 Min Mo 7 6 36 7 0.44
516 Juan Yu 6 5 58 10 0.44
517 Lei Wan 3 3 100 21 0.44
518 Ping Jia 5 5 66 12 0.44
519 Ming Ren 6 6 51 7 0.44
520 Xiulan Wei 6 6 51 7 0.44
521 Xia Ding 5 5 62 13 0.43
522 Wei Luo 7 7 36 2 0.43
523 Qiang Dai 6 6 47 8 0.43
524 Mike Robinson 4 4 80 17 0.43
525 Lei Luo 6 6 54 6 0.43
526 Dwayne Wilson 7 7 28 4 0.43
527 Wei Shi 6 6 50 7 0.43
528 Barbara Weitzel 4 4 61 22 0.43
529 Li Deng 7 6 41 5 0.43
530 Brandi Rodriguez 5 5 35 20 0.43
531 Na Lu 3 3 105 19 0.43
532 Ping Song 7 7 31 3 0.43
533 Xia Chen 7 7 31 3 0.43
534 Xia Xue 6 6 42 9 0.43
535 Tao Su 6 6 53 6 0.43
536 Silvio Krause 7 7 16 7 0.43
537 Gang Tan 7 7 34 2 0.43
538 Jing Long 6 6 52 6 0.43
539 Jing Lin 5 5 63 12 0.43
540 Xiulan Lin 6 6 48 7 0.43
541 Tao Hu 6 6 44 8 0.43
542 Xiuying Ye 6 6 55 5 0.43
543 Angelina Sölzer 5 5 55 14 0.43
544 Denise Bell 5 5 40 18 0.43
545 Janice Lamb 6 6 25 13 0.43
546 Jie Su 7 7 36 1 0.43
547 Xia Zhu 6 6 47 7 0.43
548 Xiulan Dong 6 6 47 7 0.43
549 Yong Wang 6 6 43 8 0.43
550 Xiulan Yan 6 6 46 7 0.43
551 Nada Pechel 4 4 68 19 0.43
552 Ping Hao 7 7 27 3 0.43
553 David Wilkerson 8 7 7 4 0.43
554 Audrey-Camille Nicolas 8 7 10 3 0.42
555 Fang Cui 5 4 69 14 0.42
556 Gang Pan 7 7 22 4 0.42
557 Christine Gonzales 6 6 44 7 0.42
558 Fang Kong 6 6 44 7 0.42
559 Wei Du 4 4 55 22 0.42
560 Juan Zou 6 6 47 6 0.42
561 Chao Yu 6 6 58 3 0.42
562 Xiuying Fang 7 6 38 4 0.42
563 Gang Yin 6 6 43 7 0.42
564 Starseed 5 5 54 13 0.42
565 Yan Qiu 7 6 45 2 0.42
566 Juan Xiong 4 4 83 14 0.42
567 Lorraine Martinez 7 7 20 4 0.42
568 Chao Xiao 6 6 42 7 0.42
569 Guiying Shao 5 5 64 10 0.42
570 Gino Michelangeli 6 6 38 8 0.42
571 Midnight Protocol 2 2 108 25 0.42
572 Lei Yang 6 6 41 7 0.42
573 Orla Seabloom 8 8 0 0 0.42
574 Yong Yan 6 6 40 7 0.42
575 Li Zeng 6 6 43 6 0.42
576 Ping Xiang 6 6 39 7 0.41
577 Clayton Reed 6 6 24 11 0.41
578 Chao Zhao 6 6 46 5 0.41
579 Fang Mao 7 7 20 3 0.41
580 Ming Yao 7 7 31 0 0.41
581 Qiang Gu 6 6 42 6 0.41
582 Fang Zhou 6 5 55 7 0.41
583 Juan Jin 5 5 60 10 0.41
584 Ivy Echos 11 4 13 1 0.41
585 Donald Choi 5 5 48 13 0.41
586 Guiying Huang 4 4 77 14 0.41
587 Yong Jia 6 6 51 3 0.41
588 Jun Ding 4 4 62 18 0.41
589 Melanie Jimenez 7 7 10 5 0.41
590 Jun Xie 6 6 39 6 0.41
591 Ping Qin 6 6 39 6 0.41
592 Jing Lai 5 5 57 10 0.41
593 Tao Hou 5 5 57 10 0.41
594 Yang He 5 5 68 7 0.41
595 Chao Wei 6 6 38 6 0.41
596 Jie Zhang 6 6 38 6 0.41
597 Kayla Scott 3 3 86 20 0.41
598 Leo Eberth 6 5 51 7 0.41
599 Robert Knight 5 5 56 10 0.41
600 Fang Lu 5 5 67 7 0.41
601 Yan Zhou 7 6 35 2 0.40
602 Gang Cao 5 5 51 11 0.40
603 David Armstrong 6 6 36 6 0.40
604 Xiulan Mo 6 6 43 4 0.40
605 Patricia Humphrey 2 1 115 25 0.40
606 Vera Rivero Palomo 2 2 94 26 0.40
607 Yolanda Barroso Pineda 2 2 94 26 0.40
608 Kelsey Campbell 6 6 27 8 0.40
609 Jie Zhu 6 6 38 5 0.40
610 Fang Chen 6 6 34 6 0.40
611 Na Yang 5 5 56 9 0.40
612 Xiulan Liu 4 4 78 12 0.40
613 Wei Han 6 5 47 7 0.40
614 Xiuying Cai 5 3 75 13 0.40
615 Tao Fang 6 6 37 5 0.40
616 Matthew Griffin 5 5 48 11 0.40
617 Min Wen 5 5 59 8 0.40
618 Wei Wang 5 5 59 8 0.40
619 Yang Gu 6 6 44 3 0.40
620 Ping Li 5 5 62 7 0.40
621 Guglielmo Canetta 6 6 25 8 0.40
622 Nicola Bernetti 6 6 25 8 0.40
623 Serena Lettiere 6 6 25 8 0.40
624 Vincentio Gentili 6 6 25 8 0.40
625 Xiuying Deng 6 6 36 5 0.40
626 Na Mao 3 3 91 17 0.40
627 Wei Su 6 6 39 4 0.40
628 Xia Xia 5 5 50 10 0.40
629 The Fiddle & The Fjord 12 3 4 1 0.40
630 Xiuying Qiu 6 6 42 3 0.40
631 Xia Fu 7 7 16 1 0.40
632 Francesca Maglio 6 6 30 6 0.39
633 Qiang Chang 5 5 63 6 0.39
634 Rei Ito 4 4 74 12 0.39
635 Min Hou 6 6 33 5 0.39
636 Tao Wei 6 6 33 5 0.39
637 Ping Yao 5 5 33 14 0.39
638 Gang Long 4 4 66 14 0.39
639 Xia Yuan 5 4 68 9 0.39
640 Joseph Dixon 3 3 106 12 0.39
641 Radiation Pulse 3 3 106 12 0.39
642 Ming Yan 4 4 69 13 0.39
643 Jing Ren 6 6 43 2 0.39
644 Jie Cai 6 6 35 4 0.39
645 Monica Brown 5 5 57 7 0.39
646 Jing Chang 5 5 53 8 0.39
647 Fang Liang 6 6 38 3 0.39
648 Lei Kong 6 6 34 4 0.39
649 Qiang Tan 3 3 93 15 0.39
650 Gang Dong 4 3 73 16 0.39
651 Jing Sun 5 5 52 8 0.39
652 Jing Mao 5 4 58 11 0.39
653 Xiuying Bai 6 6 37 3 0.39
654 Ping Wu 6 6 33 4 0.39
655 Arnaude Joseph-Lacombe 6 5 39 7 0.39
656 Guiying Tian 5 5 55 7 0.39
657 Ming Mao 5 5 55 7 0.39
658 Juan Xu 5 4 61 10 0.39
659 Yan Tan 6 6 29 5 0.39
660 Jing Qian 5 5 51 8 0.39
661 Yang Liao 5 5 51 8 0.39
662 Ashley Cox PhD 4 4 69 12 0.38
663 Blueprints 4 4 69 12 0.38
664 Primal Rhythm 4 4 69 12 0.38
665 Sara Parker 5 5 54 7 0.38
666 Jie Fang 5 5 57 6 0.38
667 Juan Mo 5 4 48 13 0.38
668 Chao Yang 5 5 53 7 0.38
669 Ming Yang 6 6 30 4 0.38
670 Helen Simon 5 5 30 13 0.38
671 Luminous Fracture 3 3 78 18 0.38
672 Robert Garcia 3 3 78 18 0.38
673 Wei Cheng 5 5 41 10 0.38
674 Jing Tan 6 6 33 3 0.38
675 Qiang Xie 5 5 55 6 0.38
676 Jie Cao 7 6 24 1 0.38
677 Ming Zhao 6 4 52 7 0.38
678 Monica Nelson 5 5 51 7 0.38
679 Heidi Jones 4 4 62 13 0.38
680 Min Shi 6 6 36 2 0.38
681 Xia Liang 6 6 32 3 0.38
682 Chao Li 6 4 44 9 0.38
683 Harro Trapp-Caspar 5 5 28 13 0.38
684 Gang Zhang 5 5 50 7 0.38
685 Ming Cheng 6 5 41 5 0.38
686 Yang Bai 5 5 46 8 0.38
687 Ping Yan 3 2 100 16 0.38
688 Na Kong 5 5 57 5 0.38
689 Juan Dong 6 6 31 3 0.38
690 Lei Zheng 6 6 31 3 0.38
691 Andrew Dawson 4 4 42 18 0.38
692 Xiulan Du 6 6 38 1 0.38
693 Juan Ding 6 6 34 2 0.38
694 Lei Long 5 5 45 8 0.38
695 Caroline Reilly 3 2 99 16 0.38
696 Lori Waters 3 2 99 16 0.38
697 Necropolis Noir 3 2 99 16 0.38
698 Jasmine Adams 4 4 67 11 0.38
699 Guiying Shen 5 5 48 7 0.37
700 Karl-August Beyer 4 4 55 14 0.37
701 Kelsey Hernandez 6 6 29 3 0.37
702 Yong Lu 5 5 40 9 0.37
703 Min Qiu 5 5 51 6 0.37
704 Jie Long 6 6 25 4 0.37
705 Qiang Zhang 6 6 25 4 0.37
706 Na Zou 5 5 47 7 0.37
707 Anthony Ortega 3 3 58 22 0.37
708 Lei Zhao 6 6 32 2 0.37
709 Erica Walton 5 4 34 15 0.37
710 Qiang Wu 5 5 50 6 0.37
711 Tobias Etzold-Dörr 5 5 50 6 0.37
712 Kristin Anderson 4 4 61 12 0.37
713 Rachel Hill 5 5 42 8 0.37
714 Li Yin 5 5 53 5 0.37
715 Denise White 4 4 53 14 0.37
716 Twilight Riders 4 4 53 14 0.37
717 Chao Han 5 5 49 6 0.37
718 Chao Xu 5 5 49 6 0.37
719 Min Su 6 6 19 5 0.37
720 Jing Qin 4 4 67 10 0.37
721 Wei Gu 6 6 30 2 0.37
722 Wei Feng 1 1 111 25 0.37
723 Ping Shen 3 3 74 17 0.37
724 Yang Wu 6 4 49 6 0.37
725 Kavya Agrawal 3 3 37 27 0.37
726 Cemil Linke 2 2 85 23 0.37
727 Fedor Bolzmann 2 2 85 23 0.37
728 Embers of Wrath 1 1 107 26 0.37
729 Anaïs Wagner 3 3 81 15 0.37
730 Neue Schicht 3 3 81 15 0.37
731 Yong Lai 5 4 50 10 0.37
732 Beatrice Albright 7 7 3 0 0.37
733 Daniel O'Connell 7 7 3 0 0.37
734 Ryan Doyle 6 6 14 6 0.37
735 The Forgotten Archives 2 2 62 29 0.37
736 Min Ren 4 3 79 11 0.37
737 Li Xue 4 4 65 10 0.37
738 Jie Zou 6 5 34 5 0.37
739 Copper Canyon Ghosts 7 7 2 0 0.37
740 Juan Cao 5 5 50 5 0.37
741 Min Huang 5 5 50 5 0.37
742 Juan Yang 4 4 61 11 0.37
743 Guiying Yao 6 6 24 3 0.37
744 Jing Zhong 6 6 24 3 0.37
745 Chao Peng 6 5 37 4 0.36
746 Guiying Cao 5 5 42 7 0.36
747 Chao Chen 6 6 23 3 0.36
748 Jing Yi 5 5 41 7 0.36
749 Jun Jin 4 4 63 10 0.36
750 Margarita Henschel 5 5 33 9 0.36
751 Fredo Grossi 5 5 44 6 0.36
752 Yasmin Verschut 6 6 7 7 0.36
753 Xia Cai 5 5 40 7 0.36
754 Li He 6 6 25 2 0.36
755 Julia Carter 8 6 0 0 0.36
756 Kristi Crane 6 5 16 9 0.36
757 Jun Song 4 4 69 8 0.36
758 Lei Feng 3 3 69 17 0.36
759 Yan Duan 5 5 43 6 0.36
760 Li Han 3 3 65 18 0.36
761 Gang Lei 5 5 39 7 0.36
762 Jun Pan 5 5 39 7 0.36
763 Xiuying Mo 5 5 39 7 0.36
764 Qiang Qian 6 5 37 3 0.36
765 Yang Hou 6 6 27 1 0.36
766 Jie Ding 5 5 38 7 0.36
767 Jakub Dziuda 6 6 12 5 0.36
768 Yong Cao 5 5 45 5 0.36
769 Yan He 5 5 41 6 0.36
770 Angelita Belda Enríquez 5 5 37 7 0.36
771 Gang Du 6 5 28 5 0.36
772 Cheryl Carter 5 5 33 8 0.36
773 Min Xiong 5 5 44 5 0.36
774 Min Sun 5 5 40 6 0.35
775 Gang Jia 5 5 51 3 0.35
776 Graziella Saffi 6 6 14 4 0.35
777 Yang Zhong 5 5 47 4 0.35
778 Gang Mo 6 5 16 8 0.35
779 Susan Anderson 3 3 69 16 0.35
780 Ming Zheng 5 5 43 5 0.35
781 Juan Song 6 6 28 0 0.35
782 Charles Vazquez 5 5 39 6 0.35
783 Liwia Olbrych 6 5 19 7 0.35
784 Fang Xiang 6 6 24 1 0.35
785 Tao Wen 5 5 35 7 0.35
786 Jeffery Bailey 2 2 83 21 0.35
787 Ping Zhou 6 6 20 2 0.35
788 Bartek Krężołek 6 6 16 3 0.35
789 Martine Normand 6 6 12 4 0.35
790 Li Dong 4 4 60 9 0.35
791 Jie Hu 5 5 30 8 0.35
792 Vito Stroh 5 5 30 8 0.35
793 Yan Zhu 5 5 33 7 0.35
794 Jivika Tank 5 5 14 12 0.35
795 Qiang Han 6 6 21 1 0.35
796 Laura Sanchez 6 6 6 5 0.35
797 Na Dai 6 5 34 2 0.35
798 Karina Hess 5 5 35 6 0.35
799 Yang Yao 4 3 63 12 0.35
800 Dominic Riehl 4 3 33 20 0.35
801 Jun Luo 5 5 38 5 0.35
802 Lei Cao 6 6 19 1 0.34
803 Yong Chen 4 4 52 10 0.34
804 Lisa Martin 3 3 74 13 0.34
805 Albert Miller 5 5 37 5 0.34
806 Gang Sun 5 4 43 8 0.34
807 Chao Fan 6 5 35 1 0.34
808 Yan Lu 5 5 36 5 0.34
809 Michelle Davis 5 5 32 6 0.34
810 Juan Sun 4 4 54 9 0.34
811 Christopher Lee 5 5 28 7 0.34
812 Ping Wan 5 5 39 4 0.34
813 Min Liang 5 5 35 5 0.34
814 Yong Zeng 5 5 35 5 0.34
815 Gang Zhou 5 5 38 4 0.34
816 Min Shao 5 5 38 4 0.34
817 Xia Deng 4 4 38 13 0.34
818 Yong Gao 3 3 71 13 0.34
819 Na Wei 5 5 34 5 0.34
820 Ming Xiong 4 4 52 9 0.34
821 Na Gu 4 4 63 6 0.34
822 Juan Xiao 6 5 32 1 0.34
823 Sarah Johnson 3 3 74 12 0.34
824 Xiulan Wang 5 4 43 7 0.34
825 The Saltwater Weavers 9 4 4 0 0.34
826 Xiulan Hao 6 5 28 2 0.34
827 Ping Jiang 5 5 33 5 0.34
828 Lisa Jones 3 3 66 14 0.34
829 Ann Holland 1 1 99 23 0.34
830 Eric Beasley 1 1 99 23 0.34
831 Juan Wei 5 5 36 4 0.34
832 Klaus-Günter Kensy 4 4 43 11 0.34
833 Na Chen 4 4 50 9 0.33
834 Xiulan Yuan 4 4 50 9 0.33
835 Franck Hernandez Le Leclerc 6 5 19 4 0.33
836 Christopher Johnson 2 2 109 11 0.33
837 Fang Meng 5 5 35 4 0.33
838 Qiang Pan 5 5 35 4 0.33
839 Tao Feng 5 5 35 4 0.33
840 Xia Zhou 5 5 35 4 0.33
841 Xiulan Cao 5 5 35 4 0.33
842 Xiulan Lai 5 5 35 4 0.33
843 Guiying Jia 4 4 57 7 0.33
844 Juan Gong 6 6 16 0 0.33
845 Wei Liu 6 6 12 1 0.33
846 Asta Reising 1 1 93 24 0.33
847 Karin Metz 1 1 93 24 0.33
848 Renegade Harmony 3 3 67 13 0.33
849 Claire Holmes 8 5 0 0 0.33
850 Meike van Boven 4 4 52 8 0.33
851 Na Yi 6 5 21 3 0.33
852 Yong Tian 3 3 63 14 0.33
853 Fang Tang 5 5 37 3 0.33
854 Jing Gao 3 3 70 12 0.33
855 Yan Fang 5 5 33 4 0.33
856 Lei Ma 6 4 30 5 0.33
857 Li Cheng 5 5 36 3 0.33
858 Kelli Rodriguez 3 3 69 12 0.33
859 Li Jia 5 5 32 4 0.33
860 Ping Fan 5 5 32 4 0.33
861 Ping Xiong 2 2 80 18 0.33
862 Tao Han 2 2 80 18 0.33
863 Na Xiao 4 4 54 7 0.33
864 Tao Dong 3 3 65 13 0.33
865 Guiying Zhong 5 5 39 2 0.33
866 Juan Liu 5 5 39 2 0.33
867 Na Yin 5 5 39 2 0.33
868 Jie Wei 4 4 46 9 0.33
869 Iolanda Prada 5 5 31 4 0.33
870 Jie Fu 5 5 31 4 0.33
871 Na Li 5 5 31 4 0.33
872 Wei Xiao 5 5 27 5 0.33
873 Min Jiang 5 5 38 2 0.33
874 Yan Luo 5 5 38 2 0.33
875 Qiang Ren 3 3 71 11 0.33
876 Yong Yi 5 5 34 3 0.33
877 Guiying Lei 5 5 30 4 0.33
878 Xia Kang 5 5 37 2 0.33
879 Dunja Reinhardt 5 5 22 6 0.32
880 Tao Kang 4 4 55 6 0.32
881 Catherine Prévost 5 5 18 7 0.32
882 Lilo Cichorius 5 5 14 8 0.32
883 Tina Hobbs 3 3 62 13 0.32
884 Jun Wei 5 5 32 3 0.32
885 Qiang Xu 5 5 32 3 0.32
886 Wei Yao 5 5 32 3 0.32
887 Xia Qin 5 5 32 3 0.32
888 Jing Yang 3 3 65 12 0.32
889 Feliciana Figuerola Cruz 5 5 28 4 0.32
890 Ming Fang 3 3 72 10 0.32
891 Rusty Riggins 6 6 9 0 0.32
892 Qiang Zhou 5 5 20 6 0.32
893 Tao Guo 5 5 31 3 0.32
894 Tao Shen 5 5 31 3 0.32
895 Adrien Charles 5 4 37 6 0.32
896 Olga Labudda 4 4 31 12 0.32
897 Yong Zhu 5 5 27 4 0.32
898 Fang Du 5 5 34 2 0.32
899 Xiuying Peng 5 5 34 2 0.32
900 Janet Wood 2 1 99 16 0.32
901 Olivia Morgan 2 1 99 16 0.32
902 Ricky Evans 2 1 99 16 0.32
903 Theresa Bradley 2 1 99 16 0.32
904 The Tide Singer's Knot 8 4 6 1 0.32
905 Xiulan Zheng 5 5 30 3 0.32
906 Tiffany Gill 4 4 30 12 0.32
907 Juan Wen 5 5 26 4 0.32
908 Ping Xu 5 5 33 2 0.32
909 Gang Yu 5 4 39 5 0.32
910 Qiang Fan 5 4 39 5 0.32
911 Liquid Vortex 3 3 55 14 0.32
912 Yong Tan 4 4 40 9 0.32
913 Luis Holt 2 2 99 11 0.32
914 Silke Verda 2 2 99 11 0.32
915 Michelle Williams 4 4 47 7 0.32
916 Min Xie 4 4 47 7 0.32
917 Laurie Campbell 4 4 17 15 0.32
918 Xavier Leclerc 5 5 28 3 0.32
919 Fang Ding 5 4 30 7 0.32
920 Li Xia 5 5 31 2 0.32
921 Ryan Rodriguez 4 4 53 5 0.31
922 Marcia Davis 5 4 22 9 0.31
923 Carolyn Schultz 6 5 7 4 0.31
924 Lori Hall 5 5 12 7 0.31
925 Chao Yan 5 5 23 4 0.31
926 Lei Fan 5 5 30 2 0.31
927 Brian Gonzalez 5 5 26 3 0.31
928 Noah Overeem 5 5 26 3 0.31
929 Ryan Austin 2 2 74 17 0.31
930 Whispers & Echoes 4 4 48 6 0.31
931 Annegrete Röhrdanz 4 3 54 9 0.31
932 Juan Long 5 5 22 4 0.31
933 Na Yao 5 5 22 4 0.31
934 Yang Zheng 5 4 39 4 0.31
935 Li Qiu 2 2 81 15 0.31
936 Joshua Cruz 4 4 44 7 0.31
937 Pulse Collective 4 4 44 7 0.31
938 Li Gao 5 5 29 2 0.31
939 Min Chen 5 5 29 2 0.31
940 Guiying Tao 5 4 35 5 0.31
941 The Hollow Monarchs 7 5 4 0 0.31
942 Cassian Storm 8 4 1 1 0.31
943 Wei Cui 5 5 25 3 0.31
944 Yesenia Miller 1 1 95 20 0.31
945 Hinrich Weimer 3 3 58 12 0.31
946 Jun Xiong 5 5 21 4 0.31
947 Ming Xue 4 4 43 7 0.31
948 Guiying Yuan 5 5 28 2 0.31
949 Angelica Sullivan 2 2 98 10 0.31
950 Kay van Dooren-Bastiaense 2 2 98 10 0.31
951 Gerhild Mälzer-Albers 1 1 83 23 0.31
952 Hannelore Rosemann 1 1 83 23 0.31
953 Lei Cui 4 4 46 6 0.31
954 Jeanne Guillou 5 5 16 5 0.31
955 Sienna Fox 7 5 2 0 0.31
956 Min Guo 4 4 49 5 0.31
957 Travis Williams 5 4 29 6 0.31
958 Jie Deng 5 5 30 1 0.31
959 Bernfried Stolze 4 4 41 7 0.31
960 Min Xia 4 3 58 7 0.31
961 Jason Jordan 3 3 52 13 0.31
962 Yang Xu 2 2 81 14 0.31
963 Lei Qin 4 4 44 6 0.31
964 Yong Sun 4 4 44 6 0.31
965 David Lopez 4 4 40 7 0.31
966 Joseph Alvarado 3 3 62 10 0.31
967 Min Cui 5 5 25 2 0.31
968 Ming He 5 5 25 2 0.31
969 Yan Yuan 5 5 25 2 0.31
970 Willow Sage 7 5 0 0 0.31
971 Laura Dillon 1 1 80 23 0.30
972 Nicholas Boyd 1 1 80 23 0.30
973 Wei Cao 4 4 43 6 0.30
974 Gundula Löchel 3 3 54 12 0.30
975 Phantom Harmony 3 3 54 12 0.30
976 Qiang Du 5 5 28 1 0.30
977 Yong Tang 5 5 28 1 0.30
978 Shelby Fletcher 2 1 82 18 0.30
979 Jun Fu 4 4 50 4 0.30
980 Ming Wan 5 5 24 2 0.30
981 Wei Huang 5 5 24 2 0.30
982 Juan Qiu 4 4 46 5 0.30
983 Lei Sun 5 5 20 3 0.30
984 Intermezzo Collective 3 3 53 12 0.30
985 Min Shen 5 4 22 7 0.30
986 Tao Chang 3 3 60 10 0.30
987 Fang Wei 5 5 23 2 0.30
988 Loretta Roberts 4 4 23 11 0.30
989 Ming Cao 4 4 45 5 0.30
990 Horst Barkholz 4 4 37 7 0.30
991 The Seekers 4 4 37 7 0.30
992 Emir Yohannan 2 2 37 25 0.30
993 Juan Xue 5 5 22 2 0.30
994 Joe Taylor 4 4 44 5 0.30
995 Juan Lei 5 5 18 3 0.30
996 Yang Long 4 4 40 6 0.30
997 Fang Pan 5 5 25 1 0.30
998 Li Hu 5 5 25 1 0.30
999 Xiuying Jin 4 4 36 7 0.30
1000 Qiang Peng 5 4 27 5 0.30
1001 Chao Dai 5 5 24 1 0.30
1002 Jie Yin 5 4 30 4 0.30
1003 Xiuying Gu 4 4 46 4 0.30
1004 Anne Miles 1 1 94 18 0.30
1005 Martial Harmonies 5 5 20 2 0.30
1006 Rebekka Lehmann 4 4 31 8 0.30
1007 Jun Cui 5 5 23 1 0.30
1008 Li Feng 4 4 41 5 0.29
1009 Samuel Gutierrez 4 4 41 5 0.29
1010 Guiying Mo 5 5 15 3 0.29
1011 Sean Rodgers 4 4 37 6 0.29
1012 Hazel Kara 5 5 11 4 0.29
1013 Jam & Honey 5 5 11 4 0.29
1014 Midnight Recursion 5 5 18 2 0.29
1015 Kenneth Fox 2 2 62 17 0.29
1016 Yang Xiang 3 3 47 12 0.29
1017 Artur Nurkiewicz 5 5 6 5 0.29
1018 Roberto Pausini 4 4 17 11 0.29
1019 Xiulan Yang 3 3 61 8 0.29
1020 Guiying Peng 4 4 35 6 0.29
1021 Jing Meng 4 4 35 6 0.29
1022 Xiuying Hou 4 4 35 6 0.29
1023 Xiuying Cui 3 3 46 12 0.29
1024 Fang Liu 4 4 42 4 0.29
1025 Marek Oleksa 5 5 5 5 0.29
1026 Cyprian Ziniewicz 5 4 11 8 0.29
1027 Konrad Goss 4 4 12 12 0.29
1028 Kornel Preś 4 4 12 12 0.29
1029 Wei Meng 5 5 23 0 0.29
1030 Ming Fan 4 4 34 6 0.29
1031 Ming Qiu 4 4 34 6 0.29
1032 Xiuying Du 4 3 51 6 0.29
1033 Leslie Barnes 3 3 56 9 0.29
1034 Teresa Williams 3 3 56 9 0.29
1035 Yong Gong 3 3 56 9 0.29
1036 Wei Ma 2 2 74 13 0.29
1037 Wei Hu 4 4 37 5 0.29
1038 Xiulan Fu 4 4 37 5 0.29
1039 Yan Gao 4 3 54 5 0.29
1040 Asuka Takahashi 5 5 11 3 0.29
1041 Qiang Luo 5 4 28 3 0.29
1042 Qiang Qiao 4 4 33 6 0.29
1043 Michelle Vallenduuk 5 5 7 4 0.29
1044 Fang Ren 5 5 18 1 0.29
1045 Yang Mo 5 5 18 1 0.29
1046 Guiying Chen 3 3 51 10 0.29
1047 Xiulan Jiang 5 5 14 2 0.29
1048 Erkan Barkholz 5 4 20 5 0.29
1049 Xia Lu 3 3 62 7 0.29
1050 The Brine Choir 7 4 6 0 0.29
1051 Chao Ding 4 4 36 5 0.29
1052 Insurgencia 3 3 32 15 0.29
1053 Jörg Niemeier 5 5 17 1 0.29
1054 Yan Xia 2 1 56 22 0.29
1055 Ping Ren 3 3 61 7 0.28
1056 Xiuying Zhong 4 4 24 8 0.28
1057 Ping Zhang 4 4 35 5 0.28
1058 Scott Johnson 4 4 35 5 0.28
1059 Laura Clark 5 5 9 3 0.28
1060 Li Mo 5 4 26 3 0.28
1061 Na Xu 3 3 53 9 0.28
1062 Ming Dai 5 5 16 1 0.28
1063 Min Ding 4 3 44 7 0.28
1064 Min Zhou 4 4 34 5 0.28
1065 Na Xie 4 4 34 5 0.28
1066 Narrow Passages 4 4 34 5 0.28
1067 Fang Gao 5 5 19 0 0.28
1068 Ping Tang 3 3 52 9 0.28
1069 Yan Wang 5 5 15 1 0.28
1070 Ante Wagenknecht 2 2 74 12 0.28
1071 Mariah Ramirez 4 4 37 4 0.28
1072 Yong Deng 5 5 11 2 0.28
1073 Ping Hou 3 3 59 7 0.28
1074 Silver Veil 8 3 4 0 0.28
1075 Tina Raymond 3 3 55 8 0.28
1076 Li Ren 4 4 40 3 0.28
1077 Jennifer Diaz 3 3 51 9 0.28
1078 Stefan Berus 5 5 10 2 0.28
1079 Cassette Future 7 4 2 0 0.28
1080 Brian Hughes 5 5 6 3 0.28
1081 Jun Zheng 5 5 17 0 0.28
1082 Yang Cheng 5 5 17 0 0.28
1083 Wei Ye 5 5 13 1 0.28
1084 Jeffrey Munoz PhD 2 2 61 15 0.28
1085 Thomas Salinas 2 2 61 15 0.28
1086 James Taylor 5 5 9 2 0.28
1087 Xia Guo 3 3 57 7 0.28
1088 Xia Hu 5 4 26 2 0.28
1089 Erin Fox 4 4 31 5 0.28
1090 Jun Tan 4 4 31 5 0.28
1091 Wei He 4 4 31 5 0.28
1092 Michaela Kelley 5 4 11 6 0.28
1093 Miguel Johnston 5 4 11 6 0.28
1094 Jie Kang 5 5 16 0 0.28
1095 Amy Lee 4 4 27 6 0.28
1096 Samuel Robertson 4 4 27 6 0.28
1097 Juan Xiang 4 3 44 6 0.28
1098 Yong Zhang 5 4 29 1 0.28
1099 Nela Melcer 4 4 8 11 0.28
1100 Yong Mao 4 4 30 5 0.28
1101 John Garcia 3 3 41 11 0.28
1102 Matthew Cowan 3 3 41 11 0.28
1103 Rusty Anvil 3 3 41 11 0.28
1104 Jie Tan 3 2 58 11 0.28
1105 Sophie Ramirez 5 5 15 0 0.28
1106 Xiulan Tang 5 5 15 0 0.28
1107 Jun Gong 5 4 28 1 0.28
1108 Wei Zhang 4 4 33 4 0.28
1109 Xiulan Duan 4 4 33 4 0.28
1110 Aleksander Przybyś 5 5 7 2 0.28
1111 Fang Tan 5 5 7 2 0.28
1112 Abdullah Bauer 1 1 99 13 0.27
1113 Barbara Haas 1 1 99 13 0.27
1114 Birgid Junck-Sauer 1 1 99 13 0.27
1115 Brandi Tran 1 1 99 13 0.27
1116 Brenda Hammond 1 1 99 13 0.27
1117 David Clements 1 1 99 13 0.27
1118 David Patel 1 1 99 13 0.27
1119 Gabriel Ruppert 1 1 99 13 0.27
1120 Hans-Christian Drub 1 1 99 13 0.27
1121 Keith Navarro 1 1 99 13 0.27
1122 Susan Haney 1 1 99 13 0.27
1123 Carolyn Hall 4 4 21 7 0.27
1124 Rachel Stein 2 2 69 12 0.27
1125 Fang Feng 4 4 32 4 0.27
1126 Li Zhou 4 4 32 4 0.27
1127 Matthew Yoder 4 3 23 11 0.27
1128 Na Ye 4 4 28 5 0.27
1129 Yan Qian 2 2 39 20 0.27
1130 Dariusz Olszowa 5 5 2 3 0.27
1131 Verdant Pulse 4 3 30 9 0.27
1132 Maciej Siejak 5 4 4 7 0.27
1133 Fang Hou 4 4 31 4 0.27
1134 Yang Xiao 4 4 31 4 0.27
1135 Maya Jensen 5 5 12 0 0.27
1136 Fang Gu 4 4 34 3 0.27
1137 Na Song 4 4 34 3 0.27
1138 Christine Ward 3 3 45 9 0.27
1139 Lisa Hofmann 3 3 45 9 0.27
1140 Sonora Flux 3 3 45 9 0.27
1141 Lei Cheng 2 2 67 12 0.27
1142 Guiying Tang 4 4 30 4 0.27
1143 Yong Yu 4 4 30 4 0.27
1144 Jie Jia 5 4 21 2 0.27
1145 Ping Gong 4 4 37 2 0.27
1146 Gregory Williams 3 3 48 8 0.27
1147 Fang Jia 4 4 33 3 0.27
1148 Tao Jiang 4 4 33 3 0.27
1149 Scott Sanders 3 3 44 9 0.27
1150 Jun Qiu 2 2 66 12 0.27
1151 Malwina Penar 4 4 29 4 0.27
1152 Michael Harris 4 4 29 4 0.27
1153 Xia Yu 3 3 51 7 0.27
1154 Xia Jin 4 3 42 5 0.27
1155 Wei Tian 4 4 32 3 0.27
1156 Heino Ladeck 4 4 17 7 0.27
1157 Xia Qiu 4 4 28 4 0.27
1158 Qiang Meng 4 3 34 7 0.27
1159 Anthony Gutierrez 2 2 61 13 0.27
1160 Alfred Thibault 3 3 35 11 0.27
1161 Synaptic Stream 3 3 35 11 0.27
1162 Zoé-Agnès Delaunay 3 3 35 11 0.27
1163 Finn McGraw 5 5 9 0 0.27
1164 William Tidewell 5 5 9 0 0.27
1165 Chao Yuan 4 4 31 3 0.27
1166 Jun Su 4 4 31 3 0.27
1167 Ping Du 4 4 31 3 0.27
1168 Juan Zhu 4 4 27 4 0.27
1169 Xia Song 4 4 27 4 0.27
1170 Elizabeth Saunders 4 4 23 5 0.26
1171 Jing Liao 4 4 30 3 0.26
1172 Juan Tan 4 4 30 3 0.26
1173 Na Zhu 4 4 30 3 0.26
1174 Tao Mo 4 4 30 3 0.26
1175 Xia Yao 4 4 30 3 0.26
1176 Yan Li 4 4 30 3 0.26
1177 Marcel Derek 3 3 41 9 0.26
1178 Wei Yu 3 3 41 9 0.26
1179 Ming Xiao 5 4 21 1 0.26
1180 Xiuying Guo 4 4 26 4 0.26
1181 Final Order 4 4 22 5 0.26
1182 Tao Huang 4 4 33 2 0.26
1183 Wei Tan 4 4 33 2 0.26
1184 Wei Dai 4 3 39 5 0.26
1185 Juan Lai 5 4 24 0 0.26
1186 Lei Jia 4 4 29 3 0.26
1187 Min Yao 4 4 29 3 0.26
1188 Na Liao 4 4 29 3 0.26
1189 Walter White 4 4 29 3 0.26
1190 Xiulan Deng 5 4 20 1 0.26
1191 Xiuying Lin 5 4 20 1 0.26
1192 Jennifer Gibson 2 2 62 12 0.26
1193 Laura Brown 2 2 62 12 0.26
1194 Gordon Cox 4 4 25 4 0.26
1195 Fang Qian 4 4 32 2 0.26
1196 Coralia Bellweather 5 5 6 0 0.26
1197 Lei Pan 4 4 28 3 0.26
1198 Craig Cameron 4 4 24 4 0.26
1199 Jun Wen 4 4 35 1 0.26
1200 Eugene Henderson 3 3 46 7 0.26
1201 Psycho Twang 3 3 46 7 0.26
1202 David Hayes 4 4 20 5 0.26
1203 Luciana Iacovelli 1 1 68 19 0.26
1204 Fang Zheng 4 4 31 2 0.26
1205 Wei Peng 4 3 37 5 0.26
1206 Kendra Rivera 3 3 42 8 0.26
1207 Joker's Smile 4 4 16 6 0.26
1208 Renee Williams 4 4 16 6 0.26
1209 Echoes of Arcadia 4 4 27 3 0.26
1210 Ming Li 4 4 27 3 0.26
1211 Yan Chen 3 3 38 9 0.26
1212 Jie Wan 4 4 23 4 0.26
1213 Neon Horizon 4 4 23 4 0.26
1214 Xiulan Cai 4 4 23 4 0.26
1215 Ming Xu 3 3 45 7 0.26
1216 Na Hu 3 3 45 7 0.26
1217 Ping Cui 3 3 45 7 0.26
1218 Lisa Lopez 2 2 67 10 0.26
1219 Xia Fang 4 4 30 2 0.26
1220 Yang Yang 4 4 30 2 0.26
1221 Chao Jiang 4 3 36 5 0.26
1222 Ronald Kennedy 3 3 41 8 0.26
1223 Ewan MacCrae 5 5 4 0 0.26
1224 Freya Lindholm 5 5 4 0 0.26
1225 Jun Du 4 4 26 3 0.26
1226 Ming Hu 4 3 43 3 0.26
1227 Cordula Ehlert 2 2 59 12 0.26
1228 Xia Tan 4 4 33 1 0.26
1229 Michael Holloway 3 3 44 7 0.26
1230 Fiona Mercer 5 5 3 0 0.26
1231 Liam O'Sullivan 5 5 3 0 0.26
1232 Ping Deng 4 4 25 3 0.26
1233 Xiulan Kang 4 4 25 3 0.26
1234 Anouk Schatteleijn 3 3 36 9 0.26
1235 Li Wu 4 4 17 5 0.25
1236 Dawn Gross 2 2 65 10 0.25
1237 John Gray 2 2 65 10 0.25
1238 Gang Hao 4 4 28 2 0.25
1239 Guiying Jiang 4 4 28 2 0.25
1240 Jing Song 4 3 34 5 0.25
1241 Guiying Hao 3 3 50 5 0.25
1242 Alexis Ruiz 1 1 98 10 0.25
1243 Brian Ballard 1 1 98 10 0.25
1244 Candice Santiago 1 1 98 10 0.25
1245 David Bates Jr. 1 1 98 10 0.25
1246 Eric Shelton 1 1 98 10 0.25
1247 George Williams 1 1 98 10 0.25
1248 Jared Newman 1 1 98 10 0.25
1249 Jodi Miller 1 1 98 10 0.25
1250 Jonathan Butler 1 1 98 10 0.25
1251 Joris Heerschop-Weylant 1 1 98 10 0.25
1252 Laura Christian 1 1 98 10 0.25
1253 Lauren Mccoy 1 1 98 10 0.25
1254 Paige Case 1 1 98 10 0.25
1255 Richard Mendoza 1 1 98 10 0.25
1256 Robert Lee 1 1 98 10 0.25
1257 Robert Washington 1 1 98 10 0.25
1258 Roger Porter 1 1 98 10 0.25
1259 Shelby Dixon 1 1 98 10 0.25
1260 Tanya Pittman 1 1 98 10 0.25
1261 Thomas Miller 1 1 98 10 0.25
1262 Tony Olson 1 1 98 10 0.25
1263 Virginia Warner 1 1 98 10 0.25
1264 Chao Xie 2 2 57 12 0.25
1265 Ping Tao 4 4 20 4 0.25
1266 Karin Dippel 4 4 16 5 0.25
1267 Guiying Jin 4 4 27 2 0.25
1268 Michael Moore 4 4 27 2 0.25
1269 Guiying Lin 3 3 38 8 0.25
1270 Jun Yan 4 4 23 3 0.25
1271 Lei Lai 4 4 23 3 0.25
1272 Wei Lu 4 4 23 3 0.25
1273 Dierk Bohlander 3 3 23 12 0.25
1274 Li Sun 3 2 51 9 0.25
1275 Na Yan 3 3 41 7 0.25
1276 Lei Zeng 3 2 47 10 0.25
1277 Christine Lam 4 4 15 5 0.25
1278 Jing Duan 4 4 26 2 0.25
1279 Jing Lei 4 4 26 2 0.25
1280 Christian Mcpherson 4 4 11 6 0.25
1281 Lei Yao 4 4 22 3 0.25
1282 Ping Shao 4 4 22 3 0.25
1283 Jie Yao 4 3 39 3 0.25
1284 Ryan Ford 4 4 7 7 0.25
1285 Hilmar Hänel 4 3 24 7 0.25
1286 Qiang Liu 3 3 40 7 0.25
1287 Gang Xie 4 4 25 2 0.25
1288 Lei Cai 4 4 25 2 0.25
1289 Wei Pan 4 4 25 2 0.25
1290 Na Chang 4 4 21 3 0.25
1291 Yang Han 4 4 21 3 0.25
1292 Jing Shi 4 4 28 1 0.25
1293 Jun Huang 4 4 28 1 0.25
1294 Yong Liao 3 3 39 7 0.25
1295 Jie Feng 4 4 24 2 0.25
1296 Jing Zhu 4 4 24 2 0.25
1297 Jun Xiang 4 4 24 2 0.25
1298 Shawn Fox 4 4 24 2 0.25
1299 Tao Yuan 4 4 24 2 0.25
1300 Wei Liang 4 4 24 2 0.25
1301 Xia Bai 4 4 24 2 0.25
1302 Guiying Zhang 3 3 35 8 0.25
1303 Holly Gonzalez 4 4 9 6 0.25
1304 Sovereign Elect 4 4 9 6 0.25
1305 Fang Yi 4 4 20 3 0.25
1306 Juan Cai 4 4 20 3 0.25
1307 Jun Cai 4 4 20 3 0.25
1308 Xia Wan 3 3 42 6 0.25
1309 Collettivo Moderno 2 2 53 12 0.25
1310 Lori Lee 4 4 16 4 0.25
1311 Li Kong 4 4 27 1 0.25
1312 Na Wu 4 3 33 4 0.25
1313 Jie Ma 3 3 49 4 0.25
1314 Wei Lin 4 4 23 2 0.25
1315 Guiying Fu 4 3 29 5 0.25
1316 Lisa Smith 3 3 23 11 0.25
1317 Matthew Moss 3 3 34 8 0.25
1318 Dhanuk Hari 4 4 8 6 0.25
1319 Hansh Sidhu 4 4 8 6 0.25
1320 Jie Tian 4 4 19 3 0.25
1321 Li Zhong 4 4 19 3 0.25
1322 Xiulan Tan 3 3 41 6 0.25
1323 Xia Wang 4 3 32 4 0.24
1324 Fang Qiao 3 3 48 4 0.24
1325 Ming Shen 4 4 22 2 0.24
1326 Li Tian 3 3 33 8 0.24
1327 Fang Dong 4 4 18 3 0.24
1328 Yumiko Ito 4 4 18 3 0.24
1329 Giuliana Costanzi 4 4 14 4 0.24
1330 Maura Chiappetta 4 4 14 4 0.24
1331 Chao Qian 4 4 25 1 0.24
1332 Silvia Sölzer-van der Dussen 3 3 25 10 0.24
1333 Chao Jin 4 4 21 2 0.24
1334 Dominic Phillips 4 4 21 2 0.24
1335 Jie Wang 4 4 21 2 0.24
1336 Li Tan 4 4 21 2 0.24
1337 Li Yi 4 4 21 2 0.24
1338 Min Wu 4 4 21 2 0.24
1339 Yan Xiao 4 4 21 2 0.24
1340 Karen Anderson 4 3 38 2 0.24
1341 Jie Lei 3 3 39 6 0.24
1342 Fang Xiao 4 4 24 1 0.24
1343 Tao Yu 4 4 24 1 0.24
1344 Greta Löchel-Neureuther 3 3 35 7 0.24
1345 Silvana Radisch 3 3 35 7 0.24
1346 Xiuying Cao 3 3 35 7 0.24
1347 Jing Zhou 4 4 20 2 0.24
1348 Ming Luo 4 4 20 2 0.24
1349 Xiulan Ma 4 4 20 2 0.24
1350 Jillian Salazar 2 2 49 12 0.24
1351 Jie Dong 4 4 23 1 0.24
1352 Yan Yang 4 4 23 1 0.24
1353 John Rogers 4 3 29 4 0.24
1354 Xiulan Zhou 4 3 29 4 0.24
1355 Midnight Harvest 3 3 34 7 0.24
1356 Denise Schroeder 4 4 8 5 0.24
1357 Sydney Adams 4 4 8 5 0.24
1358 Antonietta Ceravolo-Condoleo 4 4 19 2 0.24
1359 Fang Shen 4 4 19 2 0.24
1360 Jing Feng 4 4 19 2 0.24
1361 Razor's Lament 4 4 19 2 0.24
1362 Lei Fang 3 2 47 8 0.24
1363 Yang Luo 4 4 15 3 0.24
1364 Astrid Lecoq 4 4 11 4 0.24
1365 Kläre Schacht-Mielcarek 4 4 11 4 0.24
1366 Élodie Albert 4 4 11 4 0.24
1367 Li Wen 4 4 22 1 0.24
1368 Tao He 4 4 22 1 0.24
1369 James Reyes 3 3 44 4 0.24
1370 Brandon Vasquez 4 4 7 5 0.24
1371 Hex Collective 4 4 18 2 0.24
1372 Jing Ding 4 4 18 2 0.24
1373 Juan Yan 4 4 18 2 0.24
1374 Jun Wang 4 4 18 2 0.24
1375 David Miles 3 3 29 8 0.24
1376 Domenico Gasperi 3 3 40 5 0.24
1377 Jie Du 3 3 40 5 0.24
1378 Jie Wu 3 3 40 5 0.24
1379 Rozalia Ciepluch 1 1 40 23 0.24
1380 Jeffrey Phillips 4 4 14 3 0.24
1381 Lei Gao 4 4 14 3 0.24
1382 Marcus Douglas 1 1 62 17 0.24
1383 Thomas Massey 1 1 62 17 0.24
1384 Min Zou 4 3 31 3 0.24
1385 Wei Zeng 4 2 37 6 0.24
1386 Snout Noise 3 3 36 6 0.24
1387 Mathew Chapman III 3 2 42 9 0.24
1388 Guiying Su 4 4 21 1 0.24
1389 Jie Gu 4 4 21 1 0.24
1390 Li Fan 4 4 21 1 0.24
1391 Wei Dong 4 4 21 1 0.24
1392 Wei Wu 4 4 21 1 0.24
1393 Allison Sanford 1 1 54 19 0.24
1394 Cindy Smith 1 1 54 19 0.24
1395 Szymon Piguła 1 1 54 19 0.24
1396 Yan Guo 4 4 17 2 0.24
1397 Yong Qian 4 4 17 2 0.24
1398 Encrucijada 4 4 13 3 0.24
1399 Michelangelo Berlusconi 4 4 13 3 0.24
1400 Bogdan Gehringer 3 3 35 6 0.24
1401 Juan Tao 3 3 35 6 0.24
1402 Li Shen 3 3 35 6 0.24
1403 Ming Gu 3 3 35 6 0.24
1404 Ming Zhu 3 3 35 6 0.24
1405 Wendy Brown 3 3 35 6 0.24
1406 Xiuying Tian 4 4 20 1 0.23
1407 Tao Tang 4 3 22 5 0.23
1408 Chloë de Gruil 4 4 12 3 0.23
1409 Frau Adelgunde Hermighausen 4 4 12 3 0.23
1410 Ottmar Grein Groth-Butte 4 4 12 3 0.23
1411 Qiang Mo 4 3 29 3 0.23
1412 Ascendant 4 4 8 4 0.23
1413 Maria Freeman 4 4 8 4 0.23
1414 Philip Conley 2 2 56 9 0.23
1415 Fang Ma 4 4 19 1 0.23
1416 Juan Meng 4 4 19 1 0.23
1417 Lei Jiang 4 4 19 1 0.23
1418 Na Kang 4 4 19 1 0.23
1419 Yang Ma 4 4 19 1 0.23
1420 Yong Hu 4 4 19 1 0.23
1421 Kathleen Davis 3 3 19 10 0.23
1422 Jing Yin 4 4 15 2 0.23
1423 Li Shao 4 4 15 2 0.23
1424 Daniel Roberts 2 2 48 11 0.23
1425 Jessica Stafford 2 2 48 11 0.23
1426 Margaret Sanchez 2 2 48 11 0.23
1427 Andrej Ruppersberger 4 4 11 3 0.23
1428 Chao Qin 4 4 11 3 0.23
1429 Honoré Foucher 4 4 11 3 0.23
1430 Kevin Baker 4 4 11 3 0.23
1431 Maura Jovinelli 4 4 11 3 0.23
1432 Zachary Francis 4 4 11 3 0.23
1433 Lunar Drift 3 2 39 9 0.23
1434 Synaptic Collapse 4 4 7 4 0.23
1435 Shared Secrets 3 3 29 7 0.23
1436 Yong Guo 2 2 51 10 0.23
1437 Janina Kałek 4 4 14 2 0.23
1438 Lei Zhu 4 4 14 2 0.23
1439 Coriolano Luria-Scialpi 3 3 25 8 0.23
1440 Wulf Lübs 3 3 36 5 0.23
1441 Yan Chang 3 3 36 5 0.23
1442 Alexa Albers-Säuberlich 3 3 32 6 0.23
1443 Nordisk Resonans 3 3 32 6 0.23
1444 Peter Jacobi Jäckel 3 3 32 6 0.23
1445 Prisma Flux 3 3 32 6 0.23
1446 Samantha Herring 3 3 32 6 0.23
1447 Scott Mckinney 3 3 32 6 0.23
1448 Terry Gonzalez 3 3 32 6 0.23
1449 Jamie Ramos 4 4 6 4 0.23
1450 Ricky Silva 4 4 6 4 0.23
1451 Sonic Pulse 4 4 6 4 0.23
1452 Min Yin 4 4 17 1 0.23
1453 Yang Yi 4 4 17 1 0.23
1454 Gang Zeng 4 3 23 4 0.23
1455 Amanda Blake 3 3 39 4 0.23
1456 Tao Song 3 3 39 4 0.23
1457 Qiang Ye 3 3 35 5 0.23
1458 Xia Fan 3 3 35 5 0.23
1459 Yang Gao 3 3 35 5 0.23
1460 Yang Kong 4 4 20 0 0.23
1461 Qiang Xiao 4 3 26 3 0.23
1462 Li Tang 3 3 31 6 0.23
1463 Ping Cao 3 3 31 6 0.23
1464 Thomas Harrison 3 3 31 6 0.23
1465 Beth Carpenter 3 2 37 9 0.23
1466 Seth Smith 3 2 37 9 0.23
1467 Jie Dai 2 2 53 9 0.23
1468 Gang Ye 4 4 16 1 0.23
1469 Li Ye 4 4 16 1 0.23
1470 Léon Pasquier 4 4 16 1 0.23
1471 Yoko Fujita 4 4 16 1 0.23
1472 Denise Johnson 1 1 71 13 0.23
1473 Donna Malone 1 1 71 13 0.23
1474 Gang Tao 1 1 71 13 0.23
1475 Tao Zhu 4 4 19 0 0.23
1476 Justin King 3 3 30 6 0.23
1477 Ming Su 2 2 52 9 0.23
1478 Xiulan Shao 4 4 15 1 0.23
1479 Xiuying Yan 4 4 15 1 0.23
1480 Crimson Carriage 7 2 4 0 0.23
1481 Rainfall Fiction 6 3 3 0 0.23
1482 Gang Hu 3 3 33 5 0.23
1483 Guiying Yi 3 3 33 5 0.23
1484 Janine Berger 4 4 7 3 0.23
1485 Ming Cai 4 4 7 3 0.23
1486 Jun Tian 4 4 18 0 0.23
1487 Gang Mao 4 4 14 1 0.22
1488 Li Cui 4 4 14 1 0.22
1489 Tao Zou 4 4 14 1 0.22
1490 Xiulan Zou 4 4 14 1 0.22
1491 Mutual Orbit 2 2 47 10 0.22
1492 Qiang Kang 4 4 10 2 0.22
1493 Rhea Wali 4 4 10 2 0.22
1494 Adrian Carroll 2 2 21 17 0.22
1495 Christopher Rice 3 3 32 5 0.22
1496 Fallen Cherubs 3 3 32 5 0.22
1497 Yang Guo 3 3 32 5 0.22
1498 Nichole Martinez 2 2 32 14 0.22
1499 William Mcclain 4 4 6 3 0.22
1500 Ellen Ford 4 3 12 6 0.22
1501 Chao Bai 4 4 17 0 0.22
1502 Chao Ye 4 3 23 3 0.22
1503 Ping Cheng 3 3 28 6 0.22
1504 Thomas Walsh 2 2 50 9 0.22
1505 Coastal Horizon 4 4 13 1 0.22
1506 Crimson Theorem 4 4 13 1 0.22
1507 Lei Lu 4 4 13 1 0.22
1508 Na Zhang 3 3 31 5 0.22
1509 Ursus 3 3 31 5 0.22
1510 Sylas Dune 5 4 0 0 0.22
1511 Min Chang 4 4 16 0 0.22
1512 Pamela Chan 3 3 27 6 0.22
1513 Guiying Yin 4 3 18 4 0.22
1514 Nicole Sudak 4 3 18 4 0.22
1515 Henriette Gonzalez-Grégoire 1 1 60 15 0.22
1516 Laurent-Théodore Humbert 1 1 60 15 0.22
1517 Philip Schneider 1 1 60 15 0.22
1518 Édith Deschamps Le Clément 1 1 60 15 0.22
1519 Chao Jia 3 3 34 4 0.22
1520 Gang Kang 3 3 34 4 0.22
1521 Steven Smith 3 3 34 4 0.22
1522 Wei Chang 3 3 34 4 0.22
1523 Samar Agarwal 1 1 34 22 0.22
1524 Stephanie Robinson 4 4 8 2 0.22
1525 Stephen Meyer 4 4 8 2 0.22
1526 Juan Shao 4 3 25 2 0.22
1527 Surveillance 3 3 30 5 0.22
1528 Jing Shen 3 3 37 3 0.22
1529 Min Dai 3 3 37 3 0.22
1530 Shirley Riley 3 3 22 7 0.22
1531 Tao Cai 3 3 33 4 0.22
1532 Buzz Collective 4 4 7 2 0.22
1533 Diana Taylor 4 4 7 2 0.22
1534 Franck Prévost de la Bertrand 4 4 7 2 0.22
1535 Kayla Gomez 4 4 7 2 0.22
1536 Pénélope du Cohen 4 4 7 2 0.22
1537 Ming Chang 3 3 29 5 0.22
1538 Anita Hamerla 4 4 3 3 0.22
1539 Xia Tang 4 4 14 0 0.22
1540 Maria Black 3 3 25 6 0.22
1541 Yong Hao 2 2 36 12 0.22
1542 Xia Feng 3 3 32 4 0.22
1543 Jun Chen 3 3 28 5 0.22
1544 Danielle Walsh 2 2 28 14 0.22
1545 Li Zhang 2 2 28 14 0.22
1546 Kumiko Miura 4 4 2 3 0.22
1547 Ashley Yang 2 2 50 8 0.22
1548 Christopher Andrews 2 2 50 8 0.22
1549 Jeffrey Herring 2 2 50 8 0.22
1550 Li Li 4 4 13 0 0.22
1551 Wei Mao 4 4 13 0 0.22
1552 Yan Gong 3 3 24 6 0.22
1553 Yong Xue 3 3 35 3 0.22
1554 Juan Lu 2 2 46 9 0.22
1555 Xiuying Duan 4 3 22 2 0.22
1556 Arco Luminoso 3 3 27 5 0.22
1557 Ming Du 3 3 27 5 0.22
1558 Philippine Laurent 3 3 27 5 0.22
1559 Xia Qiao 3 3 27 5 0.22
1560 Ming Wen 4 4 12 0 0.22
1561 Lei Tao 4 2 35 3 0.22
1562 Aneta Pruschke-Sölzer 3 3 23 6 0.22
1563 Jessica Peck 3 3 23 6 0.22
1564 Maria Carlson 3 3 23 6 0.22
1565 Na Qin 3 3 23 6 0.22
1566 Katelyn Johnson 3 3 34 3 0.22
1567 Sarah Wheeler 3 3 34 3 0.22
1568 Donald Medina 2 2 45 9 0.21
1569 Erik Mathews 2 2 45 9 0.21
1570 Joshua Orozco 2 2 45 9 0.21
1571 Sander Veenstra 2 2 45 9 0.21
1572 Estación Sonora 3 3 30 4 0.21
1573 Guiying Gu 3 3 30 4 0.21
1574 Yang Fan 3 3 30 4 0.21
1575 Yang Liu 2 2 30 13 0.21
1576 Christina Johnson 4 4 4 2 0.21
1577 Cynthia Murphy 4 4 4 2 0.21
1578 Derek Smith 4 4 4 2 0.21
1579 Julie Davis 4 4 4 2 0.21
1580 Raissa Anders 4 4 4 2 0.21
1581 Kimberly Stanton 3 3 26 5 0.21
1582 Yan Jia 3 3 26 5 0.21
1583 Xia Pan 4 3 17 3 0.21
1584 Luigina Camuccini 3 3 22 6 0.21
1585 Yan Song 3 3 22 6 0.21
1586 Scott Snyder 2 2 33 12 0.21
1587 Jie Li 3 3 29 4 0.21
1588 Lei Huang 3 3 29 4 0.21
1589 Marina Acedo Montaña 3 3 29 4 0.21
1590 Selma Flantz 3 3 29 4 0.21
1591 Velvet Haze 3 3 29 4 0.21
1592 Yang Zeng 3 3 25 5 0.21
1593 Samantha Mills 2 2 47 8 0.21
1594 Yan Tao 2 2 47 8 0.21
1595 Maria Tyler 3 3 21 6 0.21
1596 Gang Gao 2 2 54 6 0.21
1597 Leandro Artigas Benítez 3 3 28 4 0.21
1598 Xia Tian 3 3 28 4 0.21
1599 Chao Kong 3 2 34 7 0.21
1600 Steven Buchanan 4 4 2 2 0.21
1601 Yong Fan 4 3 19 2 0.21
1602 David Moore 3 3 24 5 0.21
1603 Xiulan Zhong 3 3 24 5 0.21
1604 Yan Fu 3 3 35 2 0.21
1605 Yong Cui 2 2 46 8 0.21
1606 Xia Zhang 4 4 9 0 0.21
1607 Rolando Gagliardi 4 2 21 6 0.21
1608 Amber Bates 1 1 57 14 0.21
1609 April Schmidt 1 1 57 14 0.21
1610 Cody Patterson 1 1 57 14 0.21
1611 Colleen Velazquez 1 1 57 14 0.21
1612 Jeremy Park 1 1 57 14 0.21
1613 William Keller 1 1 57 14 0.21
1614 Alfredo Ajello 3 2 37 6 0.21
1615 Jun Liu 3 2 37 6 0.21
1616 Ethereal Chorus 2 2 49 7 0.21
1617 Makayla Stone 4 3 14 3 0.21
1618 Chao Fang 3 3 30 3 0.21
1619 Fang Wan 3 3 30 3 0.21
1620 Jun Deng 3 3 30 3 0.21
1621 Xiulan Xiang 2 2 41 9 0.21
1622 Chao Song 4 3 17 2 0.21
1623 Dennis Bush 3 3 22 5 0.21
1624 Rebecca Chapman 2 2 22 14 0.21
1625 Gang Lai 2 2 44 8 0.21
1626 Levi Holloway 4 4 7 0 0.21
1627 Jie Shen 3 3 29 3 0.21
1628 Juan Zhang 3 3 29 3 0.21
1629 Ming Liang 3 3 29 3 0.21
1630 Yan Kong 3 3 29 3 0.21
1631 Ming Shi 2 2 40 9 0.21
1632 Kelly Johnson 2 2 51 6 0.21
1633 Fang Hao 3 3 25 4 0.21
1634 Michael Pratt 3 3 25 4 0.21
1635 Xiulan Feng 3 3 25 4 0.21
1636 Ming Jia 5 2 17 2 0.21
1637 Charles Wright 2 2 32 11 0.21
1638 Jonah Calloway 4 4 6 0 0.21
1639 Marin Thorne 4 4 6 0 0.21
1640 Na Feng 4 3 23 0 0.21
1641 Tao Meng 3 3 28 3 0.21
1642 Wei Guo 3 3 28 3 0.21
1643 Michael Sexton 2 2 39 9 0.20
1644 Robert Jenkins 2 2 39 9 0.20
1645 Jie Xie 4 3 19 1 0.20
1646 Min Peng 3 3 24 4 0.20
1647 Puccio Bixio-Gatto 3 3 24 4 0.20
1648 Taichi Watanabe 3 3 24 4 0.20
1649 Kamila Ornat 2 2 20 14 0.20
1650 Marie James 2 2 20 14 0.20
1651 Mary Rodriguez 2 2 20 14 0.20
1652 Eckehard Wagner 3 3 16 6 0.20
1653 Juan Hu 3 3 27 3 0.20
1654 Ming Song 3 2 44 3 0.20
1655 Hansjürgen Börner 2 2 38 9 0.20
1656 Michael Gomez 2 2 49 6 0.20
1657 Jie He 3 3 23 4 0.20
1658 Whispered Embers 3 3 23 4 0.20
1659 Kristen Parks 3 3 19 5 0.20
1660 Whisper the Thistles 3 3 19 5 0.20
1661 Jing Li 3 3 30 2 0.20
1662 Megan Sandoval 2 2 30 11 0.20
1663 The Phantom Operators 5 3 5 0 0.20
1664 Astrid Nørgaard 4 4 4 0 0.20
1665 Barnaby Quill 4 4 4 0 0.20
1666 Jonas Fenwick 4 4 4 0 0.20
1667 Marina Grey 4 4 4 0 0.20
1668 Orin Caldwell 4 4 4 0 0.20
1669 Robin Jones 4 4 4 0 0.20
1670 Selena Marrow 4 4 4 0 0.20
1671 Silas Finch 4 4 4 0 0.20
1672 Thorne Rivenhall 4 4 4 0 0.20
1673 Lei Dai 3 3 26 3 0.20
1674 Fang Lai 2 2 48 6 0.20
1675 Christopher Murray 1 1 59 12 0.20
1676 James Rubio 1 1 59 12 0.20
1677 Meredith Walker 1 1 59 12 0.20
1678 Rebecca Gonzales 1 1 59 12 0.20
1679 Laura Braun 2 2 33 10 0.20
1680 Lisa Miller 2 2 44 7 0.20
1681 Stacey Taylor 3 3 14 6 0.20
1682 Diana Mitchell 1 1 62 11 0.20
1683 James Hall 1 1 62 11 0.20
1684 Kevin Lopez 1 1 62 11 0.20
1685 Jing Tang 3 3 25 3 0.20
1686 Juan Zheng 3 3 25 3 0.20
1687 Li Xu 3 3 25 3 0.20
1688 Min Tan 3 3 25 3 0.20
1689 Xiulan Huang 3 3 25 3 0.20
1690 Chao Chang 4 3 16 1 0.20
1691 Jie Yi 3 3 21 4 0.20
1692 Lei Lin 3 3 21 4 0.20
1693 Emmy Bonbach 4 3 12 2 0.20
1694 Herr Heinz-Walter Scheuermann 4 3 12 2 0.20
1695 Jie Cheng 4 3 12 2 0.20
1696 Paige Adams 3 3 13 6 0.20
1697 Ping Tan 3 3 24 3 0.20
1698 Qiang Zeng 3 3 24 3 0.20
1699 Stephanie Huhn 3 3 24 3 0.20
1700 Xiuying Ma 3 3 24 3 0.20
1701 Li Jiang 3 3 27 2 0.20
1702 Tao Liu 3 3 27 2 0.20
1703 Xiuying Ren 3 3 27 2 0.20
1704 Bertram Hübel 3 3 23 3 0.20
1705 Gang Tang 3 3 23 3 0.20
1706 Guiying Xiao 3 3 23 3 0.20
1707 Lei Shao 3 3 23 3 0.20
1708 Lei Shi 3 3 23 3 0.20
1709 Li Zhao 3 3 23 3 0.20
1710 Ming Wei 3 3 23 3 0.20
1711 Jie Mao 3 3 19 4 0.20
1712 Tracy Lawson 2 2 41 7 0.20
1713 Andrew Miller 1 1 52 13 0.20
1714 Robert Martinez 1 1 52 13 0.20
1715 Samuel Morrison 1 1 52 13 0.20
1716 Iron Cathedral 7 1 3 0 0.20
1717 Chao Wang 3 3 26 2 0.20
1718 Fang Jiang 3 3 26 2 0.20
1719 Oscar Marshall 3 3 26 2 0.20
1720 Ping Xie 3 3 26 2 0.20
1721 Xia Lei 3 3 26 2 0.20
1722 Xiuying Qin 3 3 26 2 0.20
1723 Kristýna Horváthová 4 4 0 0 0.20
1724 Fiesta Nocturna 3 3 22 3 0.20
1725 Harmonic Collective 3 3 22 3 0.20
1726 Robert Pham 3 3 22 3 0.20
1727 Rómulo Huertas 3 3 22 3 0.20
1728 Tao Du 3 3 22 3 0.20
1729 Tao Kong 3 3 22 3 0.20
1730 Xiulan Yin 3 3 22 3 0.20
1731 Yan Hou 3 3 22 3 0.20
1732 Yang Dai 3 3 22 3 0.20
1733 Qiang Cheng 4 3 13 1 0.19
1734 Olga Kosałka 3 3 7 7 0.19
1735 Preset Protocol 3 3 7 7 0.19
1736 Flora Karz 3 3 18 4 0.19
1737 Glide 3 3 18 4 0.19
1738 Ryan Cohen 3 3 18 4 0.19
1739 Yong Ding 3 3 18 4 0.19
1740 Guiying Dong 3 3 29 1 0.19
1741 Yui Fukuda 3 3 14 5 0.19
1742 Jing Deng 3 3 25 2 0.19
1743 Mira Everly 5 3 0 0 0.19
1744 Juan Johnson 3 3 21 3 0.19
1745 Lei Du 3 3 21 3 0.19
1746 Na Mo 3 3 21 3 0.19
1747 Carl Jones 2 2 32 9 0.19
1748 Christina Bowers 2 2 32 9 0.19
1749 Beverly Olsen 1 1 54 12 0.19
1750 Lisa Woods 1 1 54 12 0.19
1751 Rei Aoki 1 1 54 12 0.19
1752 Vasyl Jaroš 3 2 23 7 0.19
1753 Sem de Swart-van Voorst 2 2 39 7 0.19
1754 Antonietta Zaccagnini 3 3 13 5 0.19
1755 Gang Cai 3 3 24 2 0.19
1756 Gang Tian 3 3 24 2 0.19
1757 Jing Du 3 3 24 2 0.19
1758 Ming Hou 3 3 24 2 0.19
1759 Qiang Feng 3 3 24 2 0.19
1760 Xiulan Meng 3 3 24 2 0.19
1761 Ashley Andrade 1 1 57 11 0.19
1762 Darrell Wilson 1 1 57 11 0.19
1763 Laura Moore 1 1 57 11 0.19
1764 Timothy Lin 1 1 57 11 0.19
1765 Xia Long 3 3 20 3 0.19
1766 Ingbert Hertrampf 2 2 31 9 0.19
1767 Patricia-Julie Lopes 3 3 16 4 0.19
1768 Guiying Ma 3 3 23 2 0.19
1769 Jordan Baxter 3 3 23 2 0.19
1770 Natasza Dydo 3 3 23 2 0.19
1771 Yang Liang 3 3 23 2 0.19
1772 Katrina Hendricks 3 3 8 6 0.19
1773 Edward Siering 3 3 19 3 0.19
1774 Jun Yuan 3 3 19 3 0.19
1775 Lunar Pulse 3 3 19 3 0.19
1776 Vicki Morgan 3 3 19 3 0.19
1777 Gerlind Sager-Ortmann 3 2 36 3 0.19
1778 Wei Tao 2 2 41 6 0.19
1779 Yang Cao 2 2 41 6 0.19
1780 Jonathan Diaz 4 3 10 1 0.19
1781 Timothy Rios 3 3 15 4 0.19
1782 Brandi Daugherty 1 1 63 9 0.19
1783 David Mueller 1 1 63 9 0.19
1784 Donna Davidson 1 1 63 9 0.19
1785 Jennifer Thomas 1 1 63 9 0.19
1786 Joseph Choi 1 1 63 9 0.19
1787 Joseph Huang 1 1 63 9 0.19
1788 Linda Williams 1 1 63 9 0.19
1789 Peter Johnson 1 1 63 9 0.19
1790 Raymond Marshall 1 1 63 9 0.19
1791 Shannon Hughes 1 1 63 9 0.19
1792 Stacy Saunders 1 1 63 9 0.19
1793 Tiffany Acevedo 1 1 63 9 0.19
1794 Adalbert Adolph 3 3 11 5 0.19
1795 Chao Cui 3 3 22 2 0.19
1796 Na Bai 3 3 22 2 0.19
1797 Wei Yin 3 3 22 2 0.19
1798 Xiulan Gu 3 3 22 2 0.19
1799 Xiulan Jia 3 3 22 2 0.19
1800 Xiuying Wang 3 3 22 2 0.19
1801 Yan Cui 3 3 22 2 0.19
1802 Civic Ensemble 2 2 44 5 0.19
1803 Elizabeth Wagner 2 2 44 5 0.19
1804 Bianca Sagnelli-Onio 3 3 18 3 0.19
1805 Christine Wall 3 3 18 3 0.19
1806 Fernanda Guarneri 3 3 18 3 0.19
1807 Ping Lei 3 3 18 3 0.19
1808 Rhythmic Echo 3 3 18 3 0.19
1809 Leslie Moore 2 2 29 9 0.19
1810 Archipelago 3 3 14 4 0.19
1811 Yang Zou 3 3 25 1 0.19
1812 Chao Shi 3 3 21 2 0.19
1813 Guiying Zheng 3 3 21 2 0.19
1814 Jie Zhong 3 3 21 2 0.19
1815 Jun Gu 3 3 21 2 0.19
1816 Jun Shen 3 3 21 2 0.19
1817 Tao Xiong 3 3 21 2 0.19
1818 Tao Zhao 3 2 38 2 0.19
1819 Christopher Davis 3 3 17 3 0.19
1820 Jing Xiang 3 3 17 3 0.19
1821 Rhythm Brothers 3 3 17 3 0.19
1822 Yong Zhong 4 2 25 1 0.19
1823 Akemi Nakajima 3 3 13 4 0.19
1824 Annetta Passalacqua 4 3 4 2 0.19
1825 Olaf Szumny 3 3 9 5 0.19
1826 Chao Cheng 3 3 20 2 0.19
1827 Chao Shen 3 3 20 2 0.19
1828 Min Yi 3 3 20 2 0.19
1829 Yang Qiao 3 3 20 2 0.19
1830 Yong Dai 3 3 20 2 0.19
1831 Konrad Meissner 3 3 16 3 0.19
1832 Na Luo 3 3 16 3 0.19
1833 Sovereign Riders 3 3 16 3 0.19
1834 Fang Xie 2 2 38 6 0.18
1835 Min Xu 2 2 38 6 0.18
1836 Clinton Fischer 1 1 49 12 0.18
1837 David Allison 1 1 49 12 0.18
1838 Erica Lopez 1 1 49 12 0.18
1839 Franck Joly 1 1 49 12 0.18
1840 Melissa Rivas 1 1 49 12 0.18
1841 Benoît-Zacharie Gilbert 3 3 12 4 0.18
1842 Bianca Weber 3 3 12 4 0.18
1843 Margaud Chevallier 3 3 12 4 0.18
1844 Nathalie Jacquot 3 3 12 4 0.18
1845 Sly Notion 3 3 12 4 0.18
1846 Jing Hu 2 2 34 7 0.18
1847 Luminescence 3 3 8 5 0.18
1848 Marc-Raymond Durand 3 3 8 5 0.18
1849 Sync Shift 3 3 8 5 0.18
1850 Bureaucracy 3 3 19 2 0.18
1851 Karl-Otto Käster 3 3 19 2 0.18
1852 Robert Stephens 3 3 19 2 0.18
1853 Xiulan Xie 3 3 19 2 0.18
1854 Yang Xie 3 3 19 2 0.18
1855 Phantom Roots 2 2 30 8 0.18
1856 Lei Wei 3 3 15 3 0.18
1857 Yan Wu 3 3 15 3 0.18
1858 Stacie Bennett 2 2 37 6 0.18
1859 Pier Pavone-Cignaroli 1 1 48 12 0.18
1860 Charles Chase 3 3 11 4 0.18
1861 Clara Seco-Pelayo 3 3 11 4 0.18
1862 Lina Mendizábal Robledo 3 2 17 7 0.18
1863 Qiang Sun 3 3 22 1 0.18
1864 Xiulan Hu 3 3 22 1 0.18
1865 Reginald Wallace 2 2 33 7 0.18
1866 Audrey-Suzanne Ribeiro 3 3 18 2 0.18
1867 Guiying Qiu 3 3 18 2 0.18
1868 Jason Roberts 3 3 18 2 0.18
1869 Juan Yuan 3 3 18 2 0.18
1870 Lei Deng 2 2 40 5 0.18
1871 Jing Fang 2 2 36 6 0.18
1872 Xia Hao 2 2 36 6 0.18
1873 Maggie Faivre 3 3 10 4 0.18
1874 Cardboard Twenty-Five 3 3 21 1 0.18
1875 Chao Zheng 3 3 21 1 0.18
1876 Guiying Xia 3 3 21 1 0.18
1877 Yang Lai 3 3 21 1 0.18
1878 Paul Lewis 2 2 32 7 0.18
1879 Brandon Ramirez 3 3 17 2 0.18
1880 Ming Lai 3 3 17 2 0.18
1881 Xiulan Zhang 3 3 17 2 0.18
1882 James Jackson 3 2 34 2 0.18
1883 Gang Liu 3 3 13 3 0.18
1884 Guiying Chang 3 3 20 1 0.18
1885 Lei Qiao 3 3 20 1 0.18
1886 Min Wang 3 3 20 1 0.18
1887 Tao Gu 3 3 20 1 0.18
1888 Tao Long 3 3 20 1 0.18
1889 Wei Li 3 3 20 1 0.18
1890 Anthony King 3 3 16 2 0.18
1891 Echo Syntax 3 3 16 2 0.18
1892 Fang Zhu 3 3 16 2 0.18
1893 Ippazio Barcella 3 3 16 2 0.18
1894 Lei Zhong 3 3 16 2 0.18
1895 Nicole Scott 3 3 16 2 0.18
1896 Ping Dong 3 3 16 2 0.18
1897 Wei Qian 3 3 16 2 0.18
1898 Forjadura 3 2 33 2 0.18
1899 Tammy Chen 3 2 33 2 0.18
1900 Michelle Ellis 2 2 27 8 0.18
1901 Jolanda Salz 3 3 12 3 0.18
1902 Luna Spark 3 3 12 3 0.18
1903 Sayuri Sato 3 3 12 3 0.18
1904 Yang Deng 3 3 12 3 0.18
1905 David Schultz 2 2 34 6 0.18
1906 Jacqueline Dickson 2 2 34 6 0.18
1907 Jonathan Young 2 2 34 6 0.18
1908 Marianna Stefanik 3 3 8 4 0.18
1909 Fang Song 3 3 19 1 0.18
1910 Gang Qian 3 3 19 1 0.18
1911 Lei Xiao 3 3 19 1 0.18
1912 Na Cao 3 3 19 1 0.18
1913 Na Meng 3 3 19 1 0.18
1914 Tao Ding 3 3 19 1 0.18
1915 Xiulan Chang 3 3 19 1 0.18
1916 Jessica Lopez 2 2 30 7 0.18
1917 Jie Zhou 2 2 30 7 0.18
1918 Celestial Horizon 3 3 15 2 0.18
1919 Gang Zou 3 3 15 2 0.18
1920 Jing Zou 3 3 15 2 0.18
1921 Ping Qiao 3 3 15 2 0.18
1922 Ping Yu 3 3 15 2 0.18
1923 Ping Yuan 3 3 15 2 0.18
1924 Yan Lei 3 3 15 2 0.18
1925 Tao Qin 2 2 37 5 0.18
1926 Mark Martin 1 1 48 11 0.18
1927 Shawn Koch 1 1 48 11 0.18
1928 William Hansen 1 1 48 11 0.18
1929 Augustin Besnard-Imbert 3 3 11 3 0.18
1930 Diaspora Rising 3 3 11 3 0.18
1931 Dominique Perret 3 3 11 3 0.18
1932 Erdmute Hermann-Karge 3 3 11 3 0.18
1933 Gundi Barkholz 3 3 11 3 0.18
1934 Kevin Carney 3 3 11 3 0.18
1935 Lidia Szpyt 3 3 11 3 0.18
1936 Yan Zhao 3 3 22 0 0.18
1937 Kaylee Freeman 2 2 22 9 0.18
1938 Gang Huang 2 2 33 6 0.18
1939 David Stewart 1 1 44 12 0.18
1940 Juan Watkins 1 1 44 12 0.18
1941 Sheri Brooks 1 1 44 12 0.18
1942 Frau Marianna Wernecke 3 3 18 1 0.18
1943 Juan Chen 3 3 18 1 0.18
1944 Jun Qin 3 3 18 1 0.18
1945 Na Han 3 3 18 1 0.18
1946 Qiang Yu 3 3 18 1 0.18
1947 Guiying Fan 3 3 14 2 0.18
1948 Karl-Ludwig Kensy 2 2 25 8 0.18
1949 Tidal Reverie 5 2 6 0 0.18
1950 Jorge Hood 2 2 36 5 0.18
1951 Keith Hernandez 2 2 36 5 0.18
1952 Rossana Colletti 2 2 36 5 0.18
1953 Stephanie Silva 2 2 36 5 0.18
1954 Lisa Castillo 3 3 10 3 0.18
1955 Joseph Trujillo 2 2 32 6 0.17
1956 Juan Su 3 3 17 1 0.17
1957 Juan Zhou 3 3 17 1 0.17
1958 Xiuying Yu 3 3 17 1 0.17
1959 Lorenzo Morabito 3 2 23 4 0.17
1960 Cosmic Drift 3 3 13 2 0.17
1961 Julie Reynolds 3 3 13 2 0.17
1962 Jun Li 3 3 13 2 0.17
1963 Kathryn Ward 3 3 13 2 0.17
1964 Lei Peng 3 3 13 2 0.17
1965 Li Wei 3 3 13 2 0.17
1966 Tao Qiao 3 3 13 2 0.17
1967 Amber Allen 2 2 24 8 0.17
1968 Vperyod Siyanie! 2 2 24 8 0.17
1969 Yan Dai 2 2 35 5 0.17
1970 Jennifer Jackson 1 1 46 11 0.17
1971 Nancy Pierce 1 1 46 11 0.17
1972 Alex Halama 3 3 9 3 0.17
1973 Anatol Dietz 3 3 9 3 0.17
1974 Evelyn Hernandez 3 3 9 3 0.17
1975 Karina Bąbel 3 3 9 3 0.17
1976 Kathryn Sherman 3 3 9 3 0.17
1977 Whispered Strings 3 3 9 3 0.17
1978 Donna Ryan 3 3 20 0 0.17
1979 Charles Delahaye 2 2 31 6 0.17
1980 Juan Cheng 3 3 16 1 0.17
1981 Jun Long 3 3 16 1 0.17
1982 Na Jin 3 3 16 1 0.17
1983 Ping Jin 3 3 16 1 0.17
1984 Ryan Adams 3 3 16 1 0.17
1985 Wei Kang 3 3 16 1 0.17
1986 Xiuying Kang 3 3 16 1 0.17
1987 Xiuying Qian 3 3 16 1 0.17
1988 Adriana Figueroa 1 1 49 10 0.17
1989 Charles Hall 1 1 49 10 0.17
1990 James Mosley 1 1 49 10 0.17
1991 Thomas Dudley 1 1 49 10 0.17
1992 Justin Williams 3 3 12 2 0.17
1993 Ping Peng 3 3 12 2 0.17
1994 Rust & Reverie 5 2 4 0 0.17
1995 Selkie's Hollow 5 2 4 0 0.17
1996 Xiuying Sun 2 2 34 5 0.17
1997 Christopher Reed 3 3 8 3 0.17
1998 Jennifer Savage 3 3 8 3 0.17
1999 Jovens Sujos 3 3 8 3 0.17
2000 Rudolf Malý 3 3 8 3 0.17
2001 Stephen Bell 3 3 8 3 0.17
2002 Yong Bai 3 3 8 3 0.17
2003 Xia Chang 3 3 19 0 0.17
2004 Yang Duan 3 2 25 3 0.17
2005 Juan Gu 2 2 30 6 0.17
2006 Fang Chang 3 3 15 1 0.17
2007 Jing Wei 3 3 15 1 0.17
2008 Jun Feng 3 3 15 1 0.17
2009 Jun Hu 3 3 15 1 0.17
2010 Jun Peng 3 3 15 1 0.17
2011 Lei Xiong 3 3 15 1 0.17
2012 Min Wan 3 3 15 1 0.17
2013 Ming Zhong 3 3 15 1 0.17
2014 Nanami Yoshida 3 3 15 1 0.17
2015 Qiang Deng 3 3 15 1 0.17
2016 Tao Jia 3 3 15 1 0.17
2017 Xia Yang 3 3 15 1 0.17
2018 Xiuying Xia 3 3 15 1 0.17
2019 Yong Xia 3 3 11 2 0.17
2020 Violet Engines 5 2 3 0 0.17
2021 Emily Taylor 2 2 33 5 0.17
2022 Juan Zeng 2 2 33 5 0.17
2023 Na Yuan 2 2 33 5 0.17
2024 Tao Mao 2 2 33 5 0.17
2025 Jasmine Atkins 3 3 7 3 0.17
2026 Kazuya Aoki 3 3 7 3 0.17
2027 Antonietta Kreusel-Seip 3 3 18 0 0.17
2028 Xiuying Xu 3 3 18 0 0.17
2029 Kelli Williamson 2 2 29 6 0.17
2030 William Coleman 2 2 29 6 0.17
2031 Gabriel Malon 3 3 3 4 0.17
2032 Debra Martin 3 2 9 7 0.17
2033 Chao Lai 3 3 14 1 0.17
2034 Chao Liao 3 3 14 1 0.17
2035 Christine Jones 3 3 14 1 0.17
2036 Jing Kong 3 3 14 1 0.17
2037 Juan Fang 3 3 14 1 0.17
2038 Ping Guo 3 3 14 1 0.17
2039 Tao Li 3 3 14 1 0.17
2040 Tao Zheng 3 3 14 1 0.17
2041 David Rodriguez 1 1 47 10 0.17
2042 Rita Morrison 1 1 47 10 0.17
2043 Bhamini Saini 3 3 10 2 0.17
2044 Fabian Dłubak 3 3 10 2 0.17
2045 Lei Gu 3 3 10 2 0.17
2046 Leslie Mathis 3 3 10 2 0.17
2047 Patricia Hardy 3 3 10 2 0.17
2048 Phantom Syntax 3 3 10 2 0.17
2049 Turbo Kill 5 2 2 0 0.17
2050 Ming Hao 2 2 32 5 0.17
2051 Fang Xue 3 3 6 3 0.17
2052 Logan Rogers 3 3 6 3 0.17
2053 Megan Gilmore 3 3 6 3 0.17
2054 Lucas Potter 3 2 12 6 0.17
2055 Stormens Sång 3 2 12 6 0.17
2056 Jing Han 3 3 17 0 0.17
2057 Jun Yao 3 3 17 0 0.17
2058 Qiang Mao 3 3 17 0 0.17
2059 Yang Lei 3 3 17 0 0.17
2060 Peggy Krebs 2 2 28 6 0.17
2061 Na Zeng 3 3 13 1 0.17
2062 Paul Castro 3 3 13 1 0.17
2063 Tao Yan 3 3 13 1 0.17
2064 Wei Zhu 3 3 13 1 0.17
2065 Xiuying Zhou 3 3 13 1 0.17
2066 Loro 3 2 19 4 0.17
2067 Elizabeth Chase 2 2 35 4 0.17
2068 Heather Lutz 3 3 9 2 0.17
2069 Nirvaan Barman 3 3 9 2 0.17
2070 Anna Shaw 2 2 31 5 0.17
2071 Deborah Rogers 2 2 31 5 0.17
2072 Kristin Miranda 2 2 31 5 0.17
2073 Obsidian Harmony 2 2 31 5 0.17
2074 Ping Chen 2 2 31 5 0.17
2075 Yan Deng 2 2 31 5 0.17
2076 Jacqueline Fields 3 3 5 3 0.17
2077 Inès Collet 3 3 16 0 0.17
2078 Juan Xia 3 3 16 0 0.17
2079 Wei Long 3 3 16 0 0.17
2080 Joshua Oneill 2 2 27 6 0.17
2081 Ming Qian 2 2 27 6 0.17
2082 The Cedar Lane Collective 2 2 27 6 0.17
2083 Chao Guo 3 3 12 1 0.17
2084 Judith Hunter 3 3 12 1 0.17
2085 Kenneth Crosby 3 3 12 1 0.17
2086 Paula Wilson 3 3 12 1 0.17
2087 Phantom Resonance 3 3 12 1 0.17
2088 Rachel Wilson 3 3 12 1 0.17
2089 Steven Branch 3 3 12 1 0.17
2090 Matthäus Ladeck 3 2 18 4 0.17
2091 Ida Kenig 2 2 12 10 0.17
2092 Darlene Galloway 2 2 34 4 0.17
2093 Melanie Fisher 2 2 34 4 0.17
2094 Bianca Peano 2 1 40 7 0.17
2095 Yang Kang 1 1 45 10 0.17
2096 Antoine Barthelemy 3 3 8 2 0.17
2097 Fixx'd 3 3 8 2 0.17
2098 Melina Tebaldi 3 3 8 2 0.17
2099 Richard Gutierrez 3 3 8 2 0.17
2100 Zara Trivedi 3 3 8 2 0.17
2101 Andrea Miller 2 2 19 8 0.17
2102 Holly Valenzuela 2 2 30 5 0.17
2103 Edit Hethur 3 3 4 3 0.16
2104 Janna Jäkel 3 3 4 3 0.16
2105 Kelly Jones 3 3 4 3 0.16
2106 Krista Wallace 3 3 4 3 0.16
2107 Margit Sölzer 3 3 4 3 0.16
2108 Parallel Static 3 3 4 3 0.16
2109 Paula Mercado 3 3 4 3 0.16
2110 Restless Verse 3 3 4 3 0.16
2111 Scott Richmond 3 3 4 3 0.16
2112 Herr Arno Ebert 1 1 52 8 0.16
2113 Jeffrey Roman 1 1 52 8 0.16
2114 Marlene Scholtz 1 1 52 8 0.16
2115 Patrick Grein Groth 1 1 52 8 0.16
2116 Veronika Haering 1 1 52 8 0.16
2117 Willibert Ebert 1 1 52 8 0.16
2118 Chao Xiong 3 3 15 0 0.16
2119 Ming Zou 3 3 15 0 0.16
2120 Nicholas Davis 2 2 26 6 0.16
2121 Anneliese Bolnbach 3 3 11 1 0.16
2122 Gang Duan 3 3 11 1 0.16
2123 Kevin Wong 3 3 11 1 0.16
2124 Michelle Byrd 3 3 11 1 0.16
2125 Valhalla's Fury 3 3 11 1 0.16
2126 Frederic Margraf 3 2 17 4 0.16
2127 Ming Tan 2 2 22 7 0.16
2128 Wei Wen 2 2 33 4 0.16
2129 Yong Han 2 2 33 4 0.16
2130 Amber Gibson 3 3 7 2 0.16
2131 David Miller 3 3 7 2 0.16
2132 Francisco Krein-Möchlichen 3 3 7 2 0.16
2133 Lawrence Cooper 3 3 7 2 0.16
2134 Mai Sato 3 3 7 2 0.16
2135 Margaret Martin 3 3 7 2 0.16
2136 Ming Bai 3 3 7 2 0.16
2137 Ping Mao 3 3 7 2 0.16
2138 Urban Decay Theorem 3 3 7 2 0.16
2139 Brittany Kelly 2 2 29 5 0.16
2140 Christopher Roberts 2 2 29 5 0.16
2141 Fang Han 3 3 14 0 0.16
2142 Friedo Dobes 3 3 14 0 0.16
2143 Ming Shao 3 3 14 0 0.16
2144 Ming Yuan 3 3 14 0 0.16
2145 Tao Pan 3 3 14 0 0.16
2146 Lei Kang 3 2 20 3 0.16
2147 Nicole Robinson 2 2 14 9 0.16
2148 Brandon Mays 2 2 25 6 0.16
2149 Jessica Macdonald 2 2 25 6 0.16
2150 Crimson Harvest 3 3 10 1 0.16
2151 Fang Dai 3 3 10 1 0.16
2152 Jun He 3 3 10 1 0.16
2153 Li Shi 3 3 10 1 0.16
2154 Min Fan 3 3 10 1 0.16
2155 Richard Johnson 3 3 10 1 0.16
2156 Min Zhang 2 2 32 4 0.16
2157 Antonius Faust 3 3 6 2 0.16
2158 Carlo Mazzacurati 3 3 6 2 0.16
2159 Jeremy Li 3 3 6 2 0.16
2160 Joseph Burgess 3 3 6 2 0.16
2161 Misfit Bistro 3 3 6 2 0.16
2162 Rosaria Pedersoli 3 3 6 2 0.16
2163 Stephanie Garcia 3 3 6 2 0.16
2164 Jacob Harris 3 2 12 5 0.16
2165 Brandi Bates 2 2 28 5 0.16
2166 Fang Tian 3 3 13 0 0.16
2167 Juan Mao 3 3 13 0 0.16
2168 Lei Qian 3 3 13 0 0.16
2169 Ping Gu 3 3 13 0 0.16
2170 Barbara Mckee 3 3 9 1 0.16
2171 Elizabeth Johnson 3 3 9 1 0.16
2172 Erin Welch 3 3 9 1 0.16
2173 Immortal Spark 3 3 9 1 0.16
2174 Jie Liang 3 3 9 1 0.16
2175 Jing Luo 3 3 9 1 0.16
2176 Regina Bruce 3 3 9 1 0.16
2177 Min Yan 2 2 31 4 0.16
2178 Yang Wen 2 2 31 4 0.16
2179 Artisans of Rhythm 3 3 5 2 0.16
2180 Christopher Thomas 3 3 5 2 0.16
2181 Jessica Harvey 3 3 5 2 0.16
2182 Joann Johnson 3 3 5 2 0.16
2183 Laure du Hebert 3 3 5 2 0.16
2184 Michael Maddox 3 3 5 2 0.16
2185 Nathalie Legrand 3 3 5 2 0.16
2186 Pavlína Macháčková 3 3 5 2 0.16
2187 Pompeo Turchi 3 3 5 2 0.16
2188 Radiant Siblings 3 3 5 2 0.16
2189 Rati Deo 3 3 5 2 0.16
2190 Rebel Scream 3 3 5 2 0.16
2191 Sarah Gardner 3 3 5 2 0.16
2192 Timothy Bray 3 3 5 2 0.16
2193 David Johnson 3 2 22 2 0.16
2194 Angelina Ortmann 2 2 27 5 0.16
2195 Janet Margraf 2 2 27 5 0.16
2196 Juan Kong 2 2 27 5 0.16
2197 Danielle Gray 3 3 12 0 0.16
2198 Jun Gao 3 3 12 0 0.16
2199 Laurent Breton 3 3 12 0 0.16
2200 Lisa Hart 3 3 12 0 0.16
2201 Qiang Huang 3 3 12 0 0.16
2202 Astrid Gomes 3 2 18 3 0.16
2203 Li Chang 3 2 18 3 0.16
2204 Cesare Cicilia 2 2 23 6 0.16
2205 Yong Ren 2 2 23 6 0.16
2206 Diane Dunn 1 1 45 9 0.16
2207 Na Liang 1 1 45 9 0.16
2208 Gabor Salz 3 3 8 1 0.16
2209 Francesca Krebs 2 2 19 7 0.16
2210 Ronnie Cook 2 2 19 7 0.16
2211 Min Liu 2 2 30 4 0.16
2212 Amy Tate 3 3 4 2 0.16
2213 Antoine Perrot-Léger 3 3 4 2 0.16
2214 Charlotte Lefèvre 3 3 4 2 0.16
2215 Christine Bonnin-Morel 3 3 4 2 0.16
2216 Hridaan Kaur 3 3 4 2 0.16
2217 Matthieu Le Roux 3 3 4 2 0.16
2218 Operatic Insurgence 3 3 4 2 0.16
2219 Phantom Sync 3 3 4 2 0.16
2220 Rohan Shanker 3 3 4 2 0.16
2221 Rémy Denis de Duhamel 3 3 4 2 0.16
2222 Spectral Junk 3 3 4 2 0.16
2223 Talvikierros 3 3 4 2 0.16
2224 The Three Drafts 3 3 4 2 0.16
2225 Berend Suijker-Tirie 2 2 26 5 0.16
2226 Yang Yuan 2 2 26 5 0.16
2227 Heinz-Joachim Renner 1 1 37 11 0.16
2228 Chao Gao 3 3 11 0 0.16
2229 Fang Yuan 3 3 11 0 0.16
2230 Guiying He 3 3 11 0 0.16
2231 Jing Gong 3 3 11 0 0.16
2232 Ming Tian 3 3 11 0 0.16
2233 Ping Su 3 3 11 0 0.16
2234 Wei Qiu 3 3 11 0 0.16
2235 Xia Han 3 3 11 0 0.16
2236 Guiying Lai 3 3 7 1 0.16
2237 Juan Ma 3 3 7 1 0.16
2238 Juan Ye 3 3 7 1 0.16
2239 Michel Meyer 3 3 7 1 0.16
2240 Undetected 3 3 7 1 0.16
2241 Xiuying Liang 3 3 7 1 0.16
2242 Li Du 2 2 29 4 0.16
2243 Subtle Noise 2 2 29 4 0.16
2244 Inaaya Bhatia 3 3 3 2 0.16
2245 Kendra Green 3 3 3 2 0.16
2246 Nehmat Kanda 3 3 3 2 0.16
2247 Paula Bonbach 3 3 3 2 0.16
2248 Quantum Sonic Architects 3 3 3 2 0.16
2249 Robert Woods 3 3 3 2 0.16
2250 Split Persona 3 3 3 2 0.16
2251 Wieslaw Wulf 3 3 3 2 0.16
2252 Qiang Ding 3 2 20 2 0.16
2253 Ping Wen 2 2 25 5 0.16
2254 Angela Gonzalez 1 1 36 11 0.16
2255 Nicole Gonzalez 1 1 36 11 0.16
2256 Gang Xiang 3 3 10 0 0.16
2257 Guiying Cui 3 3 10 0 0.16
2258 Wei Jia 3 3 10 0 0.16
2259 Thibault Hardy 2 2 21 6 0.16
2260 Brandon Santos 2 1 27 9 0.16
2261 Brian Palmer 2 2 32 3 0.16
2262 Brittany Bush 2 2 32 3 0.16
2263 William Fuentes 2 2 32 3 0.16
2264 Bryan Garcia 3 3 6 1 0.16
2265 Colette-Jeannine Lemaître 3 3 6 1 0.16
2266 Crystal Cruz 3 3 6 1 0.16
2267 Electric Novella 3 3 6 1 0.16
2268 Emmanuel de Blot 3 3 6 1 0.16
2269 Kimberly Roberts 3 3 6 1 0.16
2270 Meik Plath 3 3 6 1 0.16
2271 Raymond Chartier 3 3 6 1 0.16
2272 Simone Parent 3 3 6 1 0.16
2273 Triassic Collective 3 3 6 1 0.16
2274 Valerie Brown 3 3 6 1 0.16
2275 Gang Dai 3 2 23 1 0.16
2276 Fang Qiu 2 2 28 4 0.16
2277 Jun Zhu 2 2 28 4 0.16
2278 Min Xue 2 2 28 4 0.16
2279 Tony Rhodes 2 2 28 4 0.16
2280 Michael Ross 3 3 2 2 0.16
2281 Wayne Malone 3 2 19 2 0.16
2282 Guiying Tan 2 2 24 5 0.16
2283 Gang Lin 3 3 9 0 0.15
2284 Juan Peng 3 3 9 0 0.15
2285 Vincent Morrison 2 2 20 6 0.15
2286 Capucine Pasquier 3 3 5 1 0.15
2287 Li Fu 3 3 5 1 0.15
2288 Ludovico Mondadori 3 3 5 1 0.15
2289 Martino Toninelli 3 3 5 1 0.15
2290 Yong Zou 3 2 22 1 0.15
2291 Kathleen Cunningham 2 2 16 7 0.15
2292 April Johnson 2 2 27 4 0.15
2293 Lara Wohlgemut 2 2 27 4 0.15
2294 Li Zou 2 2 27 4 0.15
2295 Operatic Pulse 2 2 27 4 0.15
2296 Kenichi Kobayashi 3 3 1 2 0.15
2297 Kari Sutton 1 1 45 8 0.15
2298 Jade Thompson 3 3 8 0 0.15
2299 Lila "Lilly" Hartman 3 3 8 0 0.15
2300 Adèle Bonneau 2 2 19 6 0.15
2301 Leah Anderson 2 2 19 6 0.15
2302 Wei Qiao 2 2 30 3 0.15
2303 Corinne du Bazin 3 3 4 1 0.15
2304 Dawn Ramsey 3 3 4 1 0.15
2305 Double Chance 3 3 4 1 0.15
2306 Jacob Boyd 3 3 4 1 0.15
2307 Marianne Sanchez-Hebert 3 3 4 1 0.15
2308 Sarah James 3 3 4 1 0.15
2309 Susan Mcclain 3 3 4 1 0.15
2310 Tracey Jordan 3 3 4 1 0.15
2311 Adrien Gérard 3 2 10 4 0.15
2312 Michel Fournier-Robert 3 2 10 4 0.15
2313 Guiying Gong 3 2 21 1 0.15
2314 Guiying Xie 3 2 17 2 0.15
2315 Ming Wang 3 2 17 2 0.15
2316 Mikołaj Rosłoń 3 3 7 0 0.15
2317 Na Hou 3 3 7 0 0.15
2318 Chao Zhang 2 2 29 3 0.15
2319 Amanda Hill 3 3 3 1 0.15
2320 Joseph Vincent 3 3 3 1 0.15
2321 Kaja Juroszek 3 3 3 1 0.15
2322 Karol Jereczek 3 3 3 1 0.15
2323 Lueur 3 3 3 1 0.15
2324 Timothy Burke 3 3 3 1 0.15
2325 Jonathan Wade 3 2 16 2 0.15
2326 Christopher Rosales 2 2 21 5 0.15
2327 Kuno Grein Groth 2 2 21 5 0.15
2328 Tara Jennings 2 2 21 5 0.15
2329 Wei Xia 2 2 21 5 0.15
2330 Dawn Nelson 2 2 32 2 0.15
2331 Arlo Sterling 3 3 6 0 0.15
2332 Cassian Rae 3 3 6 0 0.15
2333 Elara May 3 3 6 0 0.15
2334 Eva-Marie Seidel-Otto 3 3 6 0 0.15
2335 Lyra Blaze 3 3 6 0 0.15
2336 Orion Cruz 3 3 6 0 0.15
2337 William Roux 3 1 29 3 0.15
2338 Jing Hou 2 2 28 3 0.15
2339 Michael Richard 2 2 28 3 0.15
2340 Ryan Palmer 2 2 28 3 0.15
2341 Gioacchino Gargallo 2 1 34 6 0.15
2342 Randolf Fiebig 2 1 34 6 0.15
2343 Dorothée Rousset 1 1 39 9 0.15
2344 Amy White 3 3 2 1 0.15
2345 Eric Lewis 3 3 2 1 0.15
2346 William Jones 3 3 2 1 0.15
2347 Marija Lehmann 2 2 13 7 0.15
2348 John Hernandez 2 2 24 4 0.15
2349 Juan Shen 2 2 24 4 0.15
2350 Kimberly Ross 2 2 24 4 0.15
2351 Stacy Henderson 2 2 24 4 0.15
2352 Matthew Miller 1 1 24 13 0.15
2353 Gang Xia 3 2 15 2 0.15
2354 Daniel Rogers 2 2 31 2 0.15
2355 Caleb Foster 3 3 5 0 0.15
2356 Eleanor Wren 3 3 5 0 0.15
2357 Kai Reynolds 3 3 5 0 0.15
2358 Michael Smith 3 3 5 0 0.15
2359 Moral Friction 3 3 5 0 0.15
2360 Gunther Hendriks 2 2 16 6 0.15
2361 Gang Yao 2 2 27 3 0.15
2362 Guiying Duan 2 2 27 3 0.15
2363 Lotte Arens 2 2 27 3 0.15
2364 Ming Kong 3 2 18 1 0.15
2365 Xia Ma 3 2 18 1 0.15
2366 Erin Shah 2 2 23 4 0.15
2367 James Moore 2 2 23 4 0.15
2368 Li Yao 2 2 23 4 0.15
2369 Karol Klęczar 2 2 8 8 0.15
2370 Robin Russell 2 2 19 5 0.15
2371 Theresa Pena 1 1 19 14 0.15
2372 William Mcfarland 1 1 19 14 0.15
2373 The Wave Riders 4 2 5 0 0.15
2374 Audrey Johnson 1 1 41 8 0.15
2375 Dhruv Mall 1 1 41 8 0.15
2376 Áurea Escobar-Jara 1 1 41 8 0.15
2377 Ivy Steele 3 3 4 0 0.15
2378 Mikayla Cook 3 3 4 0 0.15
2379 Nate Wild 3 3 4 0 0.15
2380 Scarlett Moon 3 3 4 0 0.15
2381 Kristina Gonzales 2 2 15 6 0.15
2382 Danielle Luna 2 1 21 9 0.15
2383 John Pitts 2 2 26 3 0.15
2384 Xia Ye 3 2 17 1 0.15
2385 Juan Bai 2 2 22 4 0.15
2386 Nocturnal Drifters 2 2 22 4 0.15
2387 Xiulan Fang 2 2 22 4 0.15
2388 Amber Murray 1 1 44 7 0.15
2389 Autumn Moore 1 1 44 7 0.15
2390 Gabrielle Wright 1 1 44 7 0.15
2391 Kimberly Zimmerman 1 1 44 7 0.15
2392 Melissa Brown 1 1 44 7 0.15
2393 Stacy Kennedy 1 1 44 7 0.15
2394 Tina Jackson 1 1 44 7 0.15
2395 Liquid Prism 3 2 13 2 0.15
2396 Echoes of Eternity 3 3 3 0 0.14
2397 Sandra Green 3 3 3 0 0.14
2398 Tamara Solis 3 3 3 0 0.14
2399 Gregg Fuentes 3 2 9 3 0.14
2400 Mineral Breath Asphalt 3 2 9 3 0.14
2401 Jordan Collins 2 2 14 6 0.14
2402 Chao Gong 2 2 25 3 0.14
2403 Fang Qin 2 2 25 3 0.14
2404 Xiuying Luo 2 2 25 3 0.14
2405 Yang Xue 2 2 25 3 0.14
2406 Ellen Riley 1 1 25 12 0.14
2407 Silja Jacob 1 1 25 12 0.14
2408 Yang Yan 3 2 16 1 0.14
2409 Dean Stevens 2 2 21 4 0.14
2410 Ming Ye 2 2 21 4 0.14
2411 Stormheart 2 2 21 4 0.14
2412 Xiulan Zhu 2 2 21 4 0.14
2413 Xia Zheng 2 2 17 5 0.14
2414 Siren's Call 4 2 3 0 0.14
2415 Michael Bell 1 1 39 8 0.14
2416 Richard Hill 1 1 39 8 0.14
2417 Li Huang 3 2 19 0 0.14
2418 Ashley Cabrera 2 2 13 6 0.14
2419 Jazzy Wanderers 2 2 13 6 0.14
2420 Terrance Parker PhD 2 2 24 3 0.14
2421 Victoria Pierce 2 2 24 3 0.14
2422 Wei Sun 2 2 24 3 0.14
2423 Xiuying Chang 2 2 24 3 0.14
2424 Alexandra Levy 2 2 20 4 0.14
2425 Jing Xie 2 2 20 4 0.14
2426 Lauretta Tresoldi 2 2 20 4 0.14
2427 Michael Dean 2 2 20 4 0.14
2428 Adam Wallace 1 1 31 10 0.14
2429 Jennifer Stephens 1 1 31 10 0.14
2430 Yong Qiu 3 1 28 2 0.14
2431 Amanda Brock 2 2 16 5 0.14
2432 David Hernandez 2 2 16 5 0.14
2433 Karl-Heinz Bruder 2 2 16 5 0.14
2434 William Cruz 2 2 16 5 0.14
2435 Gundel Klapp 2 2 27 2 0.14
2436 Na Cai 2 2 27 2 0.14
2437 Carolyn Donovan 3 3 1 0 0.14
2438 Herr Serge Tröst 2 2 12 6 0.14
2439 Jeremi Kazberuk 2 2 12 6 0.14
2440 Chao Tang 2 2 23 3 0.14
2441 Jie Duan 2 2 23 3 0.14
2442 Min Bai 2 2 23 3 0.14
2443 Samuel Olsen 2 2 23 3 0.14
2444 The Wayfarers 2 2 23 3 0.14
2445 Xiuying Shen 2 2 23 3 0.14
2446 LyriCrest 1 1 45 6 0.14
2447 Melissa Miller 2 2 19 4 0.14
2448 Tao Duan 2 2 19 4 0.14
2449 Cosmic Reverberation 5 1 2 0 0.14
2450 Guiying Meng 2 2 26 2 0.14
2451 Timothy Schmidt 2 2 26 2 0.14
2452 Leif Bolzmann 2 2 11 6 0.14
2453 Lisa Thornton 2 2 11 6 0.14
2454 Alexandra Sauer-Ditschlerin 2 2 22 3 0.14
2455 Andrew Savage 2 2 22 3 0.14
2456 Heinz-Joachim Anders 2 2 22 3 0.14
2457 Rita Hänel 2 2 22 3 0.14
2458 Rosalia Bloch 2 2 22 3 0.14
2459 Ryan Kala 2 2 22 3 0.14
2460 Sandra Peterson 2 2 22 3 0.14
2461 Sharon Delgado 2 2 22 3 0.14
2462 Yan Zhong 2 2 22 3 0.14
2463 Chad Livingston 1 1 44 6 0.14
2464 Deanna Haney 1 1 44 6 0.14
2465 Anthony Davis 2 2 29 1 0.14
2466 Amalia Tartaglia 3 2 9 2 0.14
2467 Guiying Han 2 2 25 2 0.14
2468 Kendra Miller 2 2 25 2 0.14
2469 Yan Wan 2 2 25 2 0.14
2470 Genevieve Bell 4 2 0 0 0.14
2471 Solace Vesper 4 2 0 0 0.14
2472 Wei Xu 2 2 21 3 0.14
2473 Xia Shao 2 2 21 3 0.14
2474 Andrew Montgomery 3 2 12 1 0.14
2475 Chao Duan 3 2 12 1 0.14
2476 Ashley Patton 2 2 17 4 0.14
2477 Jessica Holmes 2 2 17 4 0.14
2478 Kylie Clarke 2 2 17 4 0.14
2479 Natasha Mcdaniel 2 2 17 4 0.14
2480 Xia Duan 2 2 17 4 0.14
2481 Xiulan Xu 2 2 17 4 0.14
2482 Victoria Williams 2 1 34 4 0.14
2483 Andrew Snyder 3 2 8 2 0.14
2484 Frédéric de la Coulon 3 2 8 2 0.14
2485 Valérie Mathieu 3 2 8 2 0.14
2486 Jasper Quinn 5 1 0 0 0.14
2487 Joe Perry 2 2 24 2 0.14
2488 Ming Pan 2 2 24 2 0.14
2489 Shannon Davis 2 2 24 2 0.14
2490 Xia Zhong 2 2 24 2 0.14
2491 Ashley Hanson 1 1 35 8 0.14
2492 Candice Torres 2 2 20 3 0.14
2493 Jun Mo 2 2 20 3 0.14
2494 Kimberly Smith 2 2 20 3 0.14
2495 Maurits van Bunschoten 2 2 20 3 0.14
2496 Paulo Oestrovsky 2 2 20 3 0.14
2497 Ping Shi 2 2 20 3 0.14
2498 Qiang Li 2 2 20 3 0.14
2499 Yong Gu 2 2 20 3 0.14
2500 Ping Kong 1 1 31 9 0.14
2501 Chao Xiang 2 2 16 4 0.14
2502 Gang Fu 2 2 16 4 0.14
2503 Mannat Johal 2 2 16 4 0.14
2504 Simone Säuberlich 2 2 16 4 0.14
2505 Yang Zhu 2 2 27 1 0.14
2506 Danny Williams 1 1 27 10 0.14
2507 Jennifer Hunt 1 1 27 10 0.14
2508 Katherine Sullivan 1 1 27 10 0.14
2509 Nicole Flynn 1 1 27 10 0.14
2510 Rachel Jones 1 1 27 10 0.14
2511 Amanda Nelson 2 2 12 5 0.14
2512 Chantal Stadelmann 2 2 12 5 0.14
2513 Dörthe Dehmel 2 2 12 5 0.14
2514 Larry Fields 2 2 12 5 0.14
2515 Stephanie Morales 2 2 12 5 0.14
2516 Thierry Labbé 2 2 12 5 0.14
2517 Kimberly Brown 2 2 23 2 0.13
2518 Li Hou 2 2 23 2 0.13
2519 Min Qiao 2 2 23 2 0.13
2520 Nicole Godard 2 2 23 2 0.13
2521 Yakshit Toor 2 2 8 6 0.13
2522 Alphons Donati-Pederiva 2 2 19 3 0.13
2523 Anthony Frost 2 2 19 3 0.13
2524 Augusto Villaverde Armengol 2 2 19 3 0.13
2525 David Jenkins 2 2 19 3 0.13
2526 Feliciano Pizarro Moll 2 2 19 3 0.13
2527 Franco Angeli 2 2 19 3 0.13
2528 Joshua Chase 2 2 19 3 0.13
2529 Jun Shi 2 2 19 3 0.13
2530 Maria Shea 2 2 19 3 0.13
2531 Stacey Campbell 2 2 19 3 0.13
2532 Todd Maldonado 2 2 19 3 0.13
2533 Jun Yang 3 2 10 1 0.13
2534 Tao Zhou 2 2 15 4 0.13
2535 John Wilkinson 3 2 6 2 0.13
2536 Whispered Alibi 3 2 6 2 0.13
2537 Chao Cai 2 2 22 2 0.13
2538 Cody Turner 2 2 22 2 0.13
2539 Stacy Watts 2 2 22 2 0.13
2540 Julita Koziarz 1 1 33 8 0.13
2541 Sylvie Scholz 2 2 7 6 0.13
2542 Aaron Ross 2 2 18 3 0.13
2543 Birthe Täsche 2 2 18 3 0.13
2544 Charlene Randolph 2 2 18 3 0.13
2545 Citrus Spirit 2 2 18 3 0.13
2546 Dolores Pardo Bárcena 2 2 18 3 0.13
2547 Fredy Lange 2 2 18 3 0.13
2548 Herr Oliver Schacht 2 2 18 3 0.13
2549 Joris Lieshout 2 2 18 3 0.13
2550 Michael Norman 2 2 18 3 0.13
2551 Pamela Roberts 2 2 18 3 0.13
2552 Pierluigi Telesio 2 2 18 3 0.13
2553 Richard Page 2 2 18 3 0.13
2554 Rubén Medina-Casas 2 2 18 3 0.13
2555 Salomón Miró Olivé 2 2 18 3 0.13
2556 Sigfrido Manu Morcillo Camino 2 2 18 3 0.13
2557 Torquato Fantini 2 2 18 3 0.13
2558 Vittorio Tamburello 2 2 18 3 0.13
2559 Paul Sanders 2 2 14 4 0.13
2560 Thomas Brown 2 2 25 1 0.13
2561 Gang Cheng 1 1 36 7 0.13
2562 Ewa Zwara 2 2 10 5 0.13
2563 Gang Zhu 2 2 21 2 0.13
2564 Matthew Brown 2 2 21 2 0.13
2565 Min Yuan 2 2 21 2 0.13
2566 Ming Yu 2 2 21 2 0.13
2567 Timothy Elliott 2 2 21 2 0.13
2568 Yan Bai 2 2 21 2 0.13
2569 Hans-Eberhard Schleich 2 1 27 5 0.13
2570 Tino Ring 1 1 32 8 0.13
2571 Daniel Mitchell 3 2 12 0 0.13
2572 Yan Cheng 3 2 12 0 0.13
2573 Crystal Sanders 2 2 17 3 0.13
2574 Ricardo Collins 2 2 17 3 0.13
2575 Tao Qian 2 2 17 3 0.13
2576 Anne Cobb 1 1 39 6 0.13
2577 Brandon Simmons 1 1 39 6 0.13
2578 Danielle Wright 1 1 39 6 0.13
2579 Lindsey Walker 1 1 39 6 0.13
2580 Tammy Norris 1 1 39 6 0.13
2581 Xiulan Song 2 2 24 1 0.13
2582 Eric Holland 1 1 35 7 0.13
2583 John Reed 1 1 35 7 0.13
2584 Kyle Smith 1 1 35 7 0.13
2585 Randy Gonzalez 1 1 35 7 0.13
2586 Ryan Woodard 1 1 35 7 0.13
2587 Caroline King 2 2 9 5 0.13
2588 Lindsay Nichols 2 2 9 5 0.13
2589 Moonlit Avenue 2 2 9 5 0.13
2590 Arsenio Saraceno-Mercati 2 2 20 2 0.13
2591 Chao Zhou 2 2 20 2 0.13
2592 Esther Schinke 2 2 20 2 0.13
2593 Fang Yao 2 2 20 2 0.13
2594 Gregor Dippel 2 2 20 2 0.13
2595 Yang Zhou 3 2 11 0 0.13
2596 Elżbieta Bociek 2 2 5 6 0.13
2597 Leah Burns 2 2 16 3 0.13
2598 Jordan Mullins 2 1 22 6 0.13
2599 Timothy Shea 2 1 22 6 0.13
2600 Christopher Martinez 1 1 27 9 0.13
2601 Russell Blake 1 1 27 9 0.13
2602 Katherine Francis 1 1 38 6 0.13
2603 Ingeborg Ullrich 2 2 12 4 0.13
2604 Jacob Brown 2 2 12 4 0.13
2605 Jonathan Wyatt 2 2 12 4 0.13
2606 Justin Doyle 2 2 12 4 0.13
2607 Sonia Suda 2 2 12 4 0.13
2608 Kari Williams 1 1 34 7 0.13
2609 Richard Morton 1 1 34 7 0.13
2610 Jessica Stewart 3 2 3 2 0.13
2611 Cheryl Waters 2 2 19 2 0.13
2612 Heather Moss 2 2 19 2 0.13
2613 Paul Bailey 2 2 19 2 0.13
2614 Qiang Wei 2 2 19 2 0.13
2615 Yong Du 2 2 19 2 0.13
2616 Amanda Smith 1 1 30 8 0.13
2617 Jonathan Hayes 1 1 30 8 0.13
2618 Terri Cortez 1 1 30 8 0.13
2619 Bibi Vos 1 1 41 5 0.13
2620 Cato van Holland 1 1 41 5 0.13
2621 Femke Mulder 1 1 41 5 0.13
2622 Femke van Dagsburg 1 1 41 5 0.13
2623 Jesper Heijmans 1 1 41 5 0.13
2624 Julie Schatteleijn-Haack 1 1 41 5 0.13
2625 Julie van der Noot 1 1 41 5 0.13
2626 Mark Hellevoort-Verbruggen 1 1 41 5 0.13
2627 Mats Bökenkamp 1 1 41 5 0.13
2628 Piotr Smoter 1 1 41 5 0.13
2629 Sofie van der Hoeven 1 1 41 5 0.13
2630 Szymon Samol 1 1 41 5 0.13
2631 Courtney Carroll 2 2 15 3 0.13
2632 Hans Georg Eigenwillig-Reising 2 2 15 3 0.13
2633 Kelly Munoz 2 2 15 3 0.13
2634 Min Hao 2 2 15 3 0.13
2635 Edward Morgan 2 1 21 6 0.13
2636 Alejandra Gould 1 1 15 12 0.13
2637 John Dean 1 1 15 12 0.13
2638 Jie Qiao 2 2 26 0 0.13
2639 Sherry Russell 2 2 11 4 0.13
2640 Tammy Mcdonald 2 2 11 4 0.13
2641 Guiying Fang 2 2 22 1 0.13
2642 Diya Swamy 3 2 2 2 0.13
2643 Christina Mendoza 2 2 7 5 0.13
2644 Candela Pallarès Artigas 2 2 18 2 0.13
2645 Fang Lei 2 2 18 2 0.13
2646 Gang Han 2 2 18 2 0.13
2647 Li Lai 2 2 18 2 0.13
2648 Miss Amy Harmon 2 2 18 2 0.13
2649 Qiang Zhu 2 2 18 2 0.13
2650 Ricarda Nélida Palmer Serra 2 2 18 2 0.13
2651 Yan Ding 2 2 18 2 0.13
2652 Aniela Mijas 1 1 18 11 0.13
2653 Agnès de Leroy 1 1 29 8 0.13
2654 Gang Jiang 2 2 14 3 0.13
2655 Kimberly Nelson 2 2 14 3 0.13
2656 Stephanie Montoya 2 2 14 3 0.13
2657 Apolonia Chlebda 1 1 14 12 0.13
2658 Mariner's Refrain 4 1 6 1 0.13
2659 David Franklin 3 2 5 1 0.13
2660 Desert Echoes 3 2 5 1 0.13
2661 Cameron Faulkner 2 2 10 4 0.13
2662 Chao Hao 2 2 10 4 0.13
2663 Peripheral Circuit 2 2 10 4 0.13
2664 Pivotal Trinity 2 2 10 4 0.13
2665 The Feast 2 2 10 4 0.13
2666 Timothy Lewis 2 2 10 4 0.13
2667 Jason Evans 2 2 21 1 0.13
2668 Courtney Phillips 1 1 32 7 0.13
2669 Kelli Turner 1 1 32 7 0.13
2670 Latasha Chavez 1 1 32 7 0.13
2671 Giacinto Maderna 2 2 6 5 0.13
2672 Janos Zimmer 2 2 6 5 0.13
2673 Krzysztof Linda 2 2 6 5 0.13
2674 Beppe Agazzi-Muratori 2 2 17 2 0.12
2675 Hans-Theo Eckbauer-Dehmel 2 2 17 2 0.12
2676 Lei Zhang 2 2 17 2 0.12
2677 Ming Feng 2 2 17 2 0.12
2678 Na Qiu 2 2 17 2 0.12
2679 Xia Gu 2 2 17 2 0.12
2680 Xia Shen 2 2 17 2 0.12
2681 Yang Lu 2 2 17 2 0.12
2682 Robert Peters 1 1 28 8 0.12
2683 Brenda Dunn 2 2 13 3 0.12
2684 Cosmic Sankey 2 2 13 3 0.12
2685 Cristina Williams 2 2 13 3 0.12
2686 Emily Glenn 2 2 13 3 0.12
2687 Ewa Kucz 2 2 13 3 0.12
2688 Jacqueline Monroe 2 2 13 3 0.12
2689 Jennifer Collins 2 2 13 3 0.12
2690 Mark Sparks 2 2 13 3 0.12
2691 Norma Huff 2 1 30 3 0.12
2692 Asuka Fukuda 2 2 9 4 0.12
2693 Crystal Norman 2 2 20 1 0.12
2694 Justin Campbell 2 2 20 1 0.12
2695 Mark Nichols 2 2 20 1 0.12
2696 Xia Qian 2 2 20 1 0.12
2697 Xiulan Lei 2 2 20 1 0.12
2698 Matthew Smith 2 1 26 4 0.12
2699 Hortense du Dijoux 2 2 16 2 0.12
2700 Jun Kong 2 2 16 2 0.12
2701 Kunigunde Pechel 2 2 16 2 0.12
2702 Li Kang 2 2 16 2 0.12
2703 Li Liu 2 2 16 2 0.12
2704 Melinda Gregory 2 2 16 2 0.12
2705 Na Shi 2 2 16 2 0.12
2706 Nicholas Chen 2 2 16 2 0.12
2707 Stacey Christensen 2 2 16 2 0.12
2708 Vincent Seifert 2 2 16 2 0.12
2709 Wei Xue 2 2 16 2 0.12
2710 Xia Su 2 2 16 2 0.12
2711 Yang Meng 2 2 16 2 0.12
2712 Erica Hernandez 1 1 27 8 0.12
2713 Jules Paris 1 1 27 8 0.12
2714 Kathy Johnson 1 1 27 8 0.12
2715 Lee Butler 1 1 27 8 0.12
2716 Livia Blewanus 1 1 27 8 0.12
2717 Antonio Johnson 2 2 12 3 0.12
2718 Brooke Olson 2 2 12 3 0.12
2719 Dina Borroni 2 2 12 3 0.12
2720 Jing Jia 2 2 12 3 0.12
2721 Joseph Figueroa 2 2 12 3 0.12
2722 Matilda Zarlino 2 2 12 3 0.12
2723 Pierangelo Papafava 2 2 12 3 0.12
2724 Silver Seraph 2 2 12 3 0.12
2725 Victoria Higgins 2 2 12 3 0.12
2726 Heather Wood 1 1 34 6 0.12
2727 Laura Jefferson 1 1 34 6 0.12
2728 Hansh Ramanathan 3 2 3 1 0.12
2729 Gregory Reeves 2 2 19 1 0.12
2730 Leo Schuster 2 2 19 1 0.12
2731 Qiang Jiang 2 2 19 1 0.12
2732 Yang Su 2 2 19 1 0.12
2733 Eric Reyes 1 1 30 7 0.12
2734 Monica Franco 1 1 30 7 0.12
2735 Sharon Gallegos 1 1 30 7 0.12
2736 Gang Chang 2 2 15 2 0.12
2737 Min Zhong 2 2 15 2 0.12
2738 Morgan Hernandez 2 2 15 2 0.12
2739 Patricia Pope 2 2 15 2 0.12
2740 Ping Liang 2 2 15 2 0.12
2741 Tao Xiang 2 2 15 2 0.12
2742 Xiuying Mao 2 2 15 2 0.12
2743 Yang Cai 2 2 15 2 0.12
2744 Yang Yin 2 2 15 2 0.12
2745 Yong Chang 2 2 15 2 0.12
2746 Michael Warner 1 1 26 8 0.12
2747 Onkar Chacko 1 1 26 8 0.12
2748 Robert Long 1 1 26 8 0.12
2749 Aaron Logan 1 1 37 5 0.12
2750 Carol Hanna 1 1 37 5 0.12
2751 Kevin Kidd 1 1 37 5 0.12
2752 Megan Dunn 1 1 37 5 0.12
2753 William Moore 1 1 37 5 0.12
2754 Andrew Lang 2 2 11 3 0.12
2755 Hanne-Lore Scholl-Heser 2 2 11 3 0.12
2756 Hildegund Wilmsen 2 2 11 3 0.12
2757 Jutta Matthäi 2 2 11 3 0.12
2758 Laetitia Petitjean 2 2 11 3 0.12
2759 Madeleine Pineau 2 2 11 3 0.12
2760 Qiang Wang 2 2 11 3 0.12
2761 Rosalie Hettner-Warmer 2 2 11 3 0.12
2762 Susan Da Silva 2 2 11 3 0.12
2763 Xia Xiang 2 2 11 3 0.12
2764 Yesenia Tyler 2 2 11 3 0.12
2765 Émilie Maillet 2 2 11 3 0.12
2766 Carl Osborne 2 2 22 0 0.12
2767 Maurice Mathieu 2 1 28 3 0.12
2768 Emily Jackson 1 1 33 6 0.12
2769 Na Wan 1 0 39 9 0.12
2770 Beverly Thompson 2 2 7 4 0.12
2771 Jennifer Hart 2 2 18 1 0.12
2772 Min Ma 2 2 18 1 0.12
2773 Benjamin Mercer 2 2 14 2 0.12
2774 Chao Cao 2 2 14 2 0.12
2775 Hansjürgen Pechel 2 2 14 2 0.12
2776 Jie Xia 2 2 14 2 0.12
2777 Lei Liu 2 2 14 2 0.12
2778 Lilian Gieß 2 2 14 2 0.12
2779 Mario Stumpf 2 2 14 2 0.12
2780 Marvin Holzapfel 2 2 14 2 0.12
2781 Ming Tang 2 2 14 2 0.12
2782 Qiang Zou 2 2 14 2 0.12
2783 Tao Ma 2 2 14 2 0.12
2784 John Bradley 1 1 25 8 0.12
2785 Kristina Allen 1 1 25 8 0.12
2786 Stephanie Fritz 1 1 25 8 0.12
2787 Emilie Rudolph 2 2 10 3 0.12
2788 Guiying Xiang 2 2 10 3 0.12
2789 Marlene Rose 2 2 10 3 0.12
2790 Robert Nunez 2 2 10 3 0.12
2791 Tammy Fields 2 2 10 3 0.12
2792 Warm Circuit 2 2 10 3 0.12
2793 Jing He 2 2 21 0 0.12
2794 Marcel Camus-Meunier 2 2 6 4 0.12
2795 Chao Luo 2 2 17 1 0.12
2796 Jie Sun 2 2 17 1 0.12
2797 Li Yuan 2 2 17 1 0.12
2798 Yang Qiu 2 2 17 1 0.12
2799 Andrzej Schottin-Mühle 2 2 13 2 0.12
2800 Chao Xue 2 2 13 2 0.12
2801 Daria Boucsein 2 2 13 2 0.12
2802 David Williams 2 2 13 2 0.12
2803 Gang Li 2 2 13 2 0.12
2804 Jean Buisson 2 2 13 2 0.12
2805 Kayla Campbell 2 2 13 2 0.12
2806 Lei Yu 2 2 13 2 0.12
2807 Min Cheng 2 2 13 2 0.12
2808 Na Yu 2 2 13 2 0.12
2809 Orhan Krein 2 2 13 2 0.12
2810 Shawn Wilson 2 2 13 2 0.12
2811 Sophia Baracca-Solimena 2 2 13 2 0.12
2812 Vincentio Galiazzo 2 2 13 2 0.12
2813 Eliza Brooks 3 2 4 0 0.12
2814 Imelda Zaguri 3 2 4 0 0.12
2815 Jasper Reed 3 2 4 0 0.12
2816 Nanni Telesio 3 2 4 0 0.12
2817 Arkadiusz Flisiak 2 2 9 3 0.12
2818 Dusan Ullmann 2 2 9 3 0.12
2819 Indranil Vala 2 2 9 3 0.12
2820 Kashvi Keer 2 2 9 3 0.12
2821 Lenn Blaak 2 2 9 3 0.12
2822 Los Leones del Valle 2 2 20 0 0.12
2823 Xia Yin 2 2 20 0 0.12
2824 Lisa Steele 1 1 31 6 0.12
2825 Elizabeth Shepherd 2 2 5 4 0.12
2826 Alma Davids 2 2 16 1 0.12
2827 Christopher Hill 2 2 16 1 0.12
2828 Guiying Liang 2 2 16 1 0.12
2829 Guiying Ye 2 2 16 1 0.12
2830 Nicole Simmons 2 2 16 1 0.12
2831 Tao Lai 2 2 16 1 0.12
2832 Yang Lin 2 2 16 1 0.12
2833 Amanda Tucker 2 2 12 2 0.12
2834 Anthony Hancock 2 2 12 2 0.12
2835 Cipriano Peranda 2 2 12 2 0.12
2836 Colleen Frank PhD 2 2 12 2 0.12
2837 Deanna Hines 2 2 12 2 0.12
2838 Henri Drubin 2 2 12 2 0.12
2839 Kristin Floyd 2 2 12 2 0.12
2840 Kristina Romero 2 2 12 2 0.12
2841 Min Zeng 2 2 12 2 0.12
2842 Nicholas Garcia 2 2 12 2 0.12
2843 Xia Meng 2 2 12 2 0.12
2844 Yan Shao 2 2 12 2 0.12
2845 Nadia van Dooren-de Jode Vastraedsd 2 1 29 2 0.12
2846 Christopher Hernandez 1 1 23 8 0.12
2847 Deborah Hernandez 1 1 34 5 0.12
2848 Gregory Thompson 1 1 34 5 0.12
2849 Hiroshi Shimizu 1 1 34 5 0.12
2850 Adam Nelson 2 2 8 3 0.12
2851 Anthony Cox 2 2 8 3 0.12
2852 Anto Hornich 2 2 8 3 0.12
2853 David Silva 2 2 8 3 0.12
2854 Elizabeth Nguyen 2 2 8 3 0.12
2855 Imelda Staude-Trupp 2 2 8 3 0.12
2856 James Green 2 2 8 3 0.12
2857 Kelly Brown 2 2 8 3 0.12
2858 Michael Hartman 2 2 8 3 0.12
2859 Steven Fox 2 2 8 3 0.12
2860 Tadeusz Langern 2 2 8 3 0.12
2861 Yang Li 2 2 19 0 0.12
2862 James Clark 1 1 30 6 0.12
2863 Chao Liang 2 2 15 1 0.12
2864 Evamaria Geisel 2 2 15 1 0.12
2865 Gang Liao 2 2 15 1 0.12
2866 Gang Qiao 2 2 15 1 0.12
2867 Guiying Kong 2 2 15 1 0.12
2868 Jing Cao 2 2 15 1 0.12
2869 Jing Ma 2 2 15 1 0.12
2870 Karen Williams 2 2 15 1 0.12
2871 Lei Guo 2 2 15 1 0.12
2872 Ping Ma 2 2 15 1 0.12
2873 Ping Xue 2 2 15 1 0.12
2874 Sonic Disintegration Protocol 2 2 15 1 0.12
2875 Xia Wen 2 2 15 1 0.12
2876 Yan Cao 2 2 15 1 0.12
2877 Georgina Piñol-Guerra 1 0 43 7 0.12
2878 Lalo Salgado Cazorla 1 0 43 7 0.12
2879 Luigina Stoppani-Sandi 1 0 43 7 0.12
2880 Arhaan Batta 2 2 11 2 0.11
2881 Billy Tyler 2 2 11 2 0.11
2882 Chao Mao 2 2 11 2 0.11
2883 Electric Twist 2 2 11 2 0.11
2884 Ida Ryfa 2 2 11 2 0.11
2885 Ivory Pulse 2 2 11 2 0.11
2886 Klara Parkitna 2 2 11 2 0.11
2887 Michael Jones 2 2 11 2 0.11
2888 Na Tang 2 2 11 2 0.11
2889 Nicholas Riddle 2 2 11 2 0.11
2890 Sarah Fernandez 2 2 11 2 0.11
2891 The Midnight Sentinels 2 2 11 2 0.11
2892 The Lunar Tides 4 1 3 0 0.11
2893 Jared Mercer 1 1 33 5 0.11
2894 Kim Knight 1 1 33 5 0.11
2895 Cilly Säuberlich 2 2 7 3 0.11
2896 Ian Foster 2 2 7 3 0.11
2897 Mehdi Hering 2 2 7 3 0.11
2898 Reinhild Krein 2 2 7 3 0.11
2899 The Chaotic Realm of Elliot Flame 2 2 7 3 0.11
2900 Tillmann Klemm 2 2 7 3 0.11
2901 Chao Hou 2 2 14 1 0.11
2902 Chao Shao 2 2 14 1 0.11
2903 Daniel Johnson 2 2 14 1 0.11
2904 Guiying Xu 2 2 14 1 0.11
2905 Kristi Bowers 2 2 14 1 0.11
2906 Lei Zou 2 2 14 1 0.11
2907 Min Zheng 2 2 14 1 0.11
2908 Qiang Hao 2 2 14 1 0.11
2909 Qiang Zheng 2 2 14 1 0.11
2910 Thomas Ball 2 2 14 1 0.11
2911 Xia Liao 2 2 14 1 0.11
2912 Xia Xu 2 2 14 1 0.11
2913 Xiuying Zou 2 2 14 1 0.11
2914 Yong Wu 2 2 14 1 0.11
2915 Zaira Balotelli-Peano 2 2 14 1 0.11
2916 Akemi Sato 2 2 10 2 0.11
2917 Duuk Schokman 2 2 10 2 0.11
2918 Fang Deng 2 2 10 2 0.11
2919 Gioele Monte 2 2 10 2 0.11
2920 Hiroshi Nakamura 2 2 10 2 0.11
2921 Juan Hao 2 2 10 2 0.11
2922 Jurre Kunen 2 2 10 2 0.11
2923 Karl-Friedrich auch Schlauchin 2 2 10 2 0.11
2924 Kenan Reichmann 2 2 10 2 0.11
2925 Lauretta Pasolini 2 2 10 2 0.11
2926 Linda Santos 2 2 10 2 0.11
2927 Lukas Waltrade Walderade-van Dam 2 2 10 2 0.11
2928 Marcin Maksimowicz 2 2 10 2 0.11
2929 Melodic Mischief 2 2 10 2 0.11
2930 Mila Hellevoort-Robbrechts Bruijne 2 2 10 2 0.11
2931 Rei Hayashi 2 2 10 2 0.11
2932 Sotaro Sato 2 2 10 2 0.11
2933 Tao Cheng 2 2 10 2 0.11
2934 Yasuhiro Yamamoto 2 2 10 2 0.11
2935 Yosuke Yamamoto 2 2 10 2 0.11
2936 Jerry Johnson 2 2 6 3 0.11
2937 Julian Biegała 2 2 6 3 0.11
2938 Gang Ren 2 2 17 0 0.11
2939 Juan Ren 2 2 17 0 0.11
2940 Falko Mielcarek 1 1 17 9 0.11
2941 Brooke Chang 2 2 13 1 0.11
2942 Carolyn Salas 2 2 13 1 0.11
2943 Guiying Deng 2 2 13 1 0.11
2944 Li Ding 2 2 13 1 0.11
2945 Min Lu 2 2 13 1 0.11
2946 Nancy Smith 2 2 13 1 0.11
2947 Qiang Liang 2 2 13 1 0.11
2948 Qiang Tian 2 2 13 1 0.11
2949 Wei Xie 2 2 13 1 0.11
2950 Xiulan Qiu 2 2 13 1 0.11
2951 Alexander Parsons 2 2 9 2 0.11
2952 Amanda Marquez 2 2 9 2 0.11
2953 Andrea Smith 2 2 9 2 0.11
2954 Charlene Lopez 2 2 9 2 0.11
2955 Frederick Freeman 2 2 9 2 0.11
2956 Jason Underwood 2 2 9 2 0.11
2957 Martin Johnson 2 2 9 2 0.11
2958 Michael Craig 2 2 9 2 0.11
2959 Ping Lin 2 2 9 2 0.11
2960 Daniel Mccormick 1 1 31 5 0.11
2961 Gabriel Gaudin 1 1 31 5 0.11
2962 Liberto Antonacci 1 1 31 5 0.11
2963 Valeria Perini 1 1 31 5 0.11
2964 Brittany Mcguire 2 2 5 3 0.11
2965 Tamara Brown 1 1 27 6 0.11
2966 Chao Ren 2 2 12 1 0.11
2967 Fang He 2 2 12 1 0.11
2968 Guiying Feng 2 2 12 1 0.11
2969 Jennifer Mccoy 2 2 12 1 0.11
2970 Jie Lin 2 2 12 1 0.11
2971 Jie Ye 2 2 12 1 0.11
2972 Jing Qiu 2 2 12 1 0.11
2973 Melissa Garcia 2 2 12 1 0.11
2974 Min Wei 2 2 12 1 0.11
2975 Ming Xie 2 2 12 1 0.11
2976 Ping Han 2 2 12 1 0.11
2977 Richard Davis 2 2 12 1 0.11
2978 Rosaria Ferrara 2 2 12 1 0.11
2979 Tao Chen 2 2 12 1 0.11
2980 Valentina Ungaretti-Pucci 2 2 12 1 0.11
2981 Xiuying Lu 2 2 12 1 0.11
2982 Yan Hu 2 2 12 1 0.11
2983 Levi Stewart 1 1 34 4 0.11
2984 Lori Delgado 1 1 34 4 0.11
2985 Whispered Resonance 3 1 9 2 0.11
2986 Amanda Morales 2 2 8 2 0.11
2987 April Brown 2 2 8 2 0.11
2988 Barbara Moore 2 2 8 2 0.11
2989 Bryan Richardson 2 2 8 2 0.11
2990 Charo Perez Castillo 2 2 8 2 0.11
2991 Daniel Collins 2 2 8 2 0.11
2992 Danny Craig 2 2 8 2 0.11
2993 Frédérique Peron 2 2 8 2 0.11
2994 Haruka Nishimura 2 2 8 2 0.11
2995 Midnight Feather 2 2 8 2 0.11
2996 Philippe Franke 2 2 8 2 0.11
2997 Shohei Ito 2 2 8 2 0.11
2998 The Rhythm Rascals 2 2 8 2 0.11
2999 Ashen Valor 5 0 1 0 0.11
3000 Amy Mack 2 1 25 2 0.11
3001 Joann Cunningham 2 1 25 2 0.11
3002 Mark Crawford 1 1 19 8 0.11
3003 Alex Thomas 2 2 4 3 0.11
3004 Amber Watson 2 2 4 3 0.11
3005 Julie Thompson 2 2 4 3 0.11
3006 Laura Lee 2 2 4 3 0.11
3007 Dolores Tasso 2 2 15 0 0.11
3008 Mark Trujillo 2 2 15 0 0.11
3009 Na Hao 2 2 15 0 0.11
3010 Renzo Dallapé 2 2 15 0 0.11
3011 Cory Wall 2 2 11 1 0.11
3012 Destiny Norman 2 2 11 1 0.11
3013 Dominique Ramos 2 2 11 1 0.11
3014 Gang Yi 2 2 11 1 0.11
3015 Jeffrey Thompson 2 2 11 1 0.11
3016 Jing Su 2 2 11 1 0.11
3017 Juan Hou 2 2 11 1 0.11
3018 Juan Luo 2 2 11 1 0.11
3019 Jun Fan 2 2 11 1 0.11
3020 Kevin Morrow 2 2 11 1 0.11
3021 Min Pan 2 2 11 1 0.11
3022 Na Fu 2 2 11 1 0.11
3023 Qiang Xiang 2 2 11 1 0.11
3024 Qiang Yan 2 2 11 1 0.11
3025 Tao Shao 2 2 11 1 0.11
3026 Xiuying Wan 2 2 11 1 0.11
3027 Yong Kong 2 2 11 1 0.11
3028 Annaliese Bolnbach 2 2 7 2 0.11
3029 Arne Baum 2 2 7 2 0.11
3030 Claudia Lee 2 2 7 2 0.11
3031 Hans-Gerhard Kuhl 2 2 7 2 0.11
3032 Hinrich Dörschner 2 2 7 2 0.11
3033 Jens-Peter Mosemann 2 2 7 2 0.11
3034 Michael Reyes 2 2 7 2 0.11
3035 Michael Rivera 2 2 7 2 0.11
3036 Tymon Miszta 2 2 7 2 0.11
3037 Crystal Morse 1 1 18 8 0.11
3038 Erik Tucker 1 1 18 8 0.11
3039 Juan Qian 2 2 14 0 0.11
3040 Sean Mcconnell 2 2 14 0 0.11
3041 Wei Fan 2 2 14 0 0.11
3042 Bruce Long 2 2 10 1 0.11
3043 Curtis Doyle 2 2 10 1 0.11
3044 Cynthia Monroe 2 2 10 1 0.11
3045 Céline Guérin 2 2 10 1 0.11
3046 Debbie Mcgrath 2 2 10 1 0.11
3047 Diana Elliott 2 2 10 1 0.11
3048 Dorota Cierpka 2 2 10 1 0.11
3049 François Coste-Daniel 2 2 10 1 0.11
3050 Gail Nguyen 2 2 10 1 0.11
3051 Hanako Yamaguchi 2 2 10 1 0.11
3052 Jamie Roberts 2 2 10 1 0.11
3053 Jeffrey Medina 2 2 10 1 0.11
3054 Jing Liang 2 2 10 1 0.11
3055 Joy Blanchard 2 2 10 1 0.11
3056 Juan Deng 2 2 10 1 0.11
3057 Li Xiong 2 2 10 1 0.11
3058 Margaux Mathieu 2 2 10 1 0.11
3059 Mary Barnes 2 2 10 1 0.11
3060 Melissa Williams 2 2 10 1 0.11
3061 Ming Lin 2 2 10 1 0.11
3062 Noah Frazier Jr. 2 2 10 1 0.11
3063 Olav Mülichen 2 2 10 1 0.11
3064 Stephanie Horton 2 2 10 1 0.11
3065 Theresa Lopez 2 2 10 1 0.11
3066 Yan Ye 2 2 10 1 0.11
3067 Élisabeth Lemoine 2 2 10 1 0.11
3068 Adam Carpenter 2 2 6 2 0.11
3069 Adam Lopez 2 2 6 2 0.11
3070 Alex Parrish 2 2 6 2 0.11
3071 Andrea Crawford 2 2 6 2 0.11
3072 Anthony Salas 2 2 6 2 0.11
3073 Benvenuto Lerner 2 2 6 2 0.11
3074 Cecilia Cusano-Squarcione 2 2 6 2 0.11
3075 Daniel Nelson 2 2 6 2 0.11
3076 Donald Davis 2 2 6 2 0.11
3077 Emily Brooks 2 2 6 2 0.11
3078 Eric Wright 2 2 6 2 0.11
3079 Frank Bonilla 2 2 6 2 0.11
3080 Gianpaolo Sibilia 2 2 6 2 0.11
3081 Giovanni Sauer 2 2 6 2 0.11
3082 Guido Dobes 2 2 6 2 0.11
3083 Holger Hiller 2 2 6 2 0.11
3084 Jennifer Smith 2 2 6 2 0.11
3085 Kristi Yu 2 2 6 2 0.11
3086 Kyle Anderson 2 2 6 2 0.11
3087 Nicole Williamson 2 2 6 2 0.11
3088 Phillip Hampton 2 2 6 2 0.11
3089 Richard Obrien 2 2 6 2 0.11
3090 Shelby Kelly 2 2 6 2 0.11
3091 Wally Beckmann 2 2 6 2 0.11
3092 Wesley Walker 2 2 6 2 0.11
3093 Chao Deng 1 1 28 5 0.11
3094 Grace Duncan 1 1 28 5 0.11
3095 Ming Fu 1 1 28 5 0.11
3096 Stephanie Wilkerson 1 1 28 5 0.11
3097 Ashley Guzman 2 2 13 0 0.11
3098 Guiying Wu 2 2 13 0 0.11
3099 Jie Ren 2 2 13 0 0.11
3100 Qiang Long 2 1 19 3 0.11
3101 Adam de Vries 1 1 24 6 0.11
3102 Ana Gonzales 1 1 24 6 0.11
3103 Aylin Sire 1 1 24 6 0.11
3104 Calogero Mezzetta 1 1 24 6 0.11
3105 Luca Hazenveld 1 1 24 6 0.11
3106 Mackenzie Brown 1 1 24 6 0.11
3107 Victoria Fletcher 1 1 24 6 0.11
3108 Alois Jäntsch-Hecker 2 2 9 1 0.11
3109 Amber Huff 2 2 9 1 0.11
3110 Chao Dong 2 2 9 1 0.11
3111 Dana Ferrell 2 2 9 1 0.11
3112 Elizabeth Griffin 2 2 9 1 0.11
3113 Jing Gu 2 2 9 1 0.11
3114 Kristi Henry 2 2 9 1 0.11
3115 Margrit Hiller-Jopich 2 2 9 1 0.11
3116 Melissa Wheeler 2 2 9 1 0.11
3117 Min Meng 2 2 9 1 0.11
3118 Ming Liu 2 2 9 1 0.11
3119 Morgan Collins 2 2 9 1 0.11
3120 Na Duan 2 2 9 1 0.11
3121 Nicolaus Fiebig 2 2 9 1 0.11
3122 Qiang Zhong 2 2 9 1 0.11
3123 Roger Simmons 2 2 9 1 0.11
3124 Sherry Powell 2 2 9 1 0.11
3125 Thomas King 2 2 9 1 0.11
3126 Wei Shao 2 2 9 1 0.11
3127 Yan Qiao 2 2 9 1 0.11
3128 Yuki Saito 2 2 9 1 0.11
3129 Adrien Techer du Voisin 2 2 5 2 0.10
3130 Barbara Smith 2 2 5 2 0.10
3131 Bouncy Gong 2 2 5 2 0.10
3132 Breanna Wright 2 2 5 2 0.10
3133 Carsten Fritsch 2 2 5 2 0.10
3134 Cheryl Delgado 2 2 5 2 0.10
3135 Christopher Powell 2 2 5 2 0.10
3136 Dawn Johnson 2 2 5 2 0.10
3137 Eugène Lemaire 2 2 5 2 0.10
3138 Franck-Georges Delattre 2 2 5 2 0.10
3139 Gang Hou 2 2 5 2 0.10
3140 Joseph Jackson 2 2 5 2 0.10
3141 Kenneth Armstrong 2 2 5 2 0.10
3142 Vittoria Mazzanti 2 2 5 2 0.10
3143 Bailey Ewing 1 1 27 5 0.10
3144 Billy Green 1 1 27 5 0.10
3145 Clayton Harper 1 1 27 5 0.10
3146 Emily Black 1 1 27 5 0.10
3147 James Price 1 1 27 5 0.10
3148 Jennifer Duarte 1 1 27 5 0.10
3149 Larry Rogers 1 1 27 5 0.10
3150 Charles Jacobs 2 2 12 0 0.10
3151 Fang Shi 2 2 12 0 0.10
3152 Juan He 2 2 12 0 0.10
3153 Lisa Shaffer 2 2 12 0 0.10
3154 Ming Han 2 2 12 0 0.10
3155 Ming Zeng 2 2 12 0 0.10
3156 Qiang Cao 2 2 12 0 0.10
3157 Qiang Qin 2 2 12 0 0.10
3158 Tao Lin 2 2 12 0 0.10
3159 Vitali Rogner 2 2 12 0 0.10
3160 Wei Zheng 2 2 12 0 0.10
3161 Xia Mao 2 2 12 0 0.10
3162 Xia Zhao 2 2 12 0 0.10
3163 Yan Yu 2 2 12 0 0.10
3164 Yong Hou 2 2 12 0 0.10
3165 Yong Qiao 2 2 12 0 0.10
3166 Andrew Morrison 2 2 8 1 0.10
3167 Billy Thompson 2 2 8 1 0.10
3168 Blazing Collective 2 2 8 1 0.10
3169 Dana Henk-Bachmann 2 2 8 1 0.10
3170 David Flores 2 2 8 1 0.10
3171 Hannah Elliott 2 2 8 1 0.10
3172 Hüseyin Paffrath 2 2 8 1 0.10
3173 James Martinez 2 2 8 1 0.10
3174 Jason Weeks 2 2 8 1 0.10
3175 Jessica Baxter 2 2 8 1 0.10
3176 Jing Lu 2 2 8 1 0.10
3177 Justin Wagner 2 2 8 1 0.10
3178 Kristiane Stadelmann 2 2 8 1 0.10
3179 Michael Aguilar 2 2 8 1 0.10
3180 Peter Goodwin 2 2 8 1 0.10
3181 Wei Lei 2 2 8 1 0.10
3182 Yang Ding 2 2 8 1 0.10
3183 Elisabet Manola Aguirre Solís 2 2 4 2 0.10
3184 Guy Martel 2 2 4 2 0.10
3185 Janet Lewis 2 2 4 2 0.10
3186 Lisa Wolfe 2 2 4 2 0.10
3187 Satomi Kobayashi 2 2 4 2 0.10
3188 Scott Flores 2 2 4 2 0.10
3189 Sharon Scott 2 2 4 2 0.10
3190 Stephen Obrien 2 2 4 2 0.10
3191 Juan Antonio Ferrando-Chico 1 1 26 5 0.10
3192 Moisés del Tirado 1 1 26 5 0.10
3193 Adah Barad 1 0 32 8 0.10
3194 Hridaan Chacko 1 0 32 8 0.10
3195 Rania Bahri 1 0 32 8 0.10
3196 Bradley Brooks 2 2 11 0 0.10
3197 Celestial Ink 2 2 11 0 0.10
3198 Fang Xu 2 2 11 0 0.10
3199 Hans-Josef Steckel-Harloff 2 2 11 0 0.10
3200 Horst-Dieter Schleich 2 2 11 0 0.10
3201 Lauren Schmitt 2 2 11 0 0.10
3202 Lei Xie 2 2 11 0 0.10
3203 Li Zhu 2 2 11 0 0.10
3204 Min Cai 2 2 11 0 0.10
3205 Xiuying Liao 2 2 11 0 0.10
3206 Yang Jin 2 2 11 0 0.10
3207 Yong Liang 2 2 11 0 0.10
3208 Jonathan Hunter 1 1 22 6 0.10
3209 Lisa Henderson 1 1 22 6 0.10
3210 Auguste-Alexandre Delattre 2 2 7 1 0.10
3211 Baccio Muti 2 2 7 1 0.10
3212 Chad Booker 2 2 7 1 0.10
3213 Christiane Langlois 2 2 7 1 0.10
3214 Franciszek Filusz 2 2 7 1 0.10
3215 Gabriel Nash 2 2 7 1 0.10
3216 Gerard Hornig 2 2 7 1 0.10
3217 Guiying Qiao 2 2 7 1 0.10
3218 Ildiko Carsten-Thies 2 2 7 1 0.10
3219 Jie Liu 2 2 7 1 0.10
3220 Krystyna Lübs 2 2 7 1 0.10
3221 Linda Guzman 2 2 7 1 0.10
3222 Marcelina Ogłaza 2 2 7 1 0.10
3223 Mary Thompson 2 2 7 1 0.10
3224 Min Lin 2 2 7 1 0.10
3225 Paul Powell 2 2 7 1 0.10
3226 Radosław Duszkiewicz 2 2 7 1 0.10
3227 Robert Norris 2 2 7 1 0.10
3228 Ulrike Ziegert 2 2 7 1 0.10
3229 Victoire Morvan 2 2 7 1 0.10
3230 Xiulan Cheng 2 2 7 1 0.10
3231 Fang Gong 1 1 29 4 0.10
3232 Jennifer Rice 1 1 29 4 0.10
3233 Jeremiah Love 2 2 3 2 0.10
3234 Kimaya Jaggi 2 2 3 2 0.10
3235 Marisa Tresoldi 2 2 3 2 0.10
3236 Miki Hasegawa 2 2 3 2 0.10
3237 Miki Miura 2 2 3 2 0.10
3238 Miłosz Staciwa 2 2 3 2 0.10
3239 Shlok Desai 2 2 3 2 0.10
3240 Vanessa Trevisani 2 2 3 2 0.10
3241 Velvet Prism 2 2 3 2 0.10
3242 Victoria Carpaccio 2 2 3 2 0.10
3243 Gang Su 2 1 20 2 0.10
3244 Jennifer Faust 1 1 25 5 0.10
3245 Wieland Käster 1 1 25 5 0.10
3246 Chao Lin 2 2 10 0 0.10
3247 Divit Srinivas 2 2 10 0 0.10
3248 Fang Xia 2 2 10 0 0.10
3249 Guiying Liu 2 2 10 0 0.10
3250 Guiying Zhao 2 2 10 0 0.10
3251 Jing Jiang 2 2 10 0 0.10
3252 Kaylee Quinn 2 2 10 0 0.10
3253 Kimberly Stewart 2 2 10 0 0.10
3254 Na Gao 2 2 10 0 0.10
3255 Na Tan 2 2 10 0 0.10
3256 Qiang Shao 2 2 10 0 0.10
3257 Tao Deng 2 2 10 0 0.10
3258 William Rodriguez 2 2 10 0 0.10
3259 Yang Dong 2 2 10 0 0.10
3260 Aaron Anderson 1 1 32 3 0.10
3261 Ashley Myers 1 1 32 3 0.10
3262 Curtis Jones 1 1 32 3 0.10
3263 David Benson 1 1 32 3 0.10
3264 Joshua Rodriguez 1 1 32 3 0.10
3265 Robert Gray 1 1 32 3 0.10
3266 William Dominguez 1 1 32 3 0.10
3267 Yang Xiong 3 1 7 1 0.10
3268 Anaya Barman 2 2 6 1 0.10
3269 Ann Nielsen 2 2 6 1 0.10
3270 Artur Garbacik 2 2 6 1 0.10
3271 Brian Contreras 2 2 6 1 0.10
3272 Christine Bartlett 2 2 6 1 0.10
3273 Colton Dickerson 2 2 6 1 0.10
3274 Damini Kata 2 2 6 1 0.10
3275 Elijah Smith 2 2 6 1 0.10
3276 Emily Nash 2 2 6 1 0.10
3277 Igor Goliasz 2 2 6 1 0.10
3278 Jivika Mahal 2 2 6 1 0.10
3279 Konrad Klinkosz 2 2 6 1 0.10
3280 Laura Beck 2 2 6 1 0.10
3281 Lei Li 2 2 6 1 0.10
3282 Lindsay Williams 2 2 6 1 0.10
3283 Margaud Morel 2 2 6 1 0.10
3284 Mary Maxwell 2 2 6 1 0.10
3285 Michelle Stafford 2 2 6 1 0.10
3286 Nicole Cook 2 2 6 1 0.10
3287 Noor van Hoevel en van Zwindrecht 2 2 6 1 0.10
3288 Patricia Myers 2 2 6 1 0.10
3289 Przemysław Szałkiewicz 2 2 6 1 0.10
3290 Richard Goodman 2 2 6 1 0.10
3291 Ryan Lin 2 2 6 1 0.10
3292 Shelly Kelly 2 2 6 1 0.10
3293 Yésica de Oller 2 2 6 1 0.10
3294 Anita Parada 1 1 17 7 0.10
3295 Larry Massey 1 1 28 4 0.10
3296 Raymond Kennedy 1 1 28 4 0.10
3297 Rene Wulff-Kranz 1 1 28 4 0.10
3298 Andrew Richard 2 2 2 2 0.10
3299 Jeremi Wita 2 2 2 2 0.10
3300 Phillip Hamilton 2 2 2 2 0.10
3301 Sonia Krochmal 2 2 2 2 0.10
3302 Sébastien Delaunay 2 2 2 2 0.10
3303 Michael Price 2 1 19 2 0.10
3304 Edwin Hansen 2 2 9 0 0.10
3305 Guiying Yang 2 2 9 0 0.10
3306 Jie Song 2 2 9 0 0.10
3307 Juan Dai 2 2 9 0 0.10
3308 Karl-Friedrich Kreusel 2 2 9 0 0.10
3309 Na Tian 2 2 9 0 0.10
3310 Na Xiang 2 2 9 0 0.10
3311 Ping Chang 2 2 9 0 0.10
3312 Wei Bai 2 2 9 0 0.10
3313 Wei Kong 2 2 9 0 0.10
3314 Xiuying Long 2 2 9 0 0.10
3315 Xiuying Shi 2 2 9 0 0.10
3316 Yan Hao 2 2 9 0 0.10
3317 Yan Ren 2 2 9 0 0.10
3318 Yang Song 2 2 9 0 0.10
3319 Ella Parker 3 1 6 1 0.10
3320 Aarav Chana 2 2 5 1 0.10
3321 Alfredo Cases Gaya 2 2 5 1 0.10
3322 Arnav Khosla 2 2 5 1 0.10
3323 Brandon Roman 2 2 5 1 0.10
3324 Carol Moyer 2 2 5 1 0.10
3325 Cengiz Knappe 2 2 5 1 0.10
3326 Cheryl Malone 2 2 5 1 0.10
3327 Christopher Long 2 2 5 1 0.10
3328 Cristina Marenzio 2 2 5 1 0.10
3329 Gregory Hartman 2 2 5 1 0.10
3330 Isa Balotelli 2 2 5 1 0.10
3331 Jason Green 2 2 5 1 0.10
3332 Jun Bai 2 2 5 1 0.10
3333 Kayla Smith 2 2 5 1 0.10
3334 Kaylee Patrick 2 2 5 1 0.10
3335 Kerri Castillo 2 2 5 1 0.10
3336 Marianna Polaszek 2 2 5 1 0.10
3337 Mary Campbell 2 2 5 1 0.10
3338 Meredith Sullivan 2 2 5 1 0.10
3339 Neon Pulse 2 2 5 1 0.10
3340 Raghav Chawla 2 2 5 1 0.10
3341 Ranbir Sangha 2 2 5 1 0.10
3342 Reyansh Bir 2 2 5 1 0.10
3343 Rouven Ernst 2 2 5 1 0.10
3344 Silent Horizon 2 2 5 1 0.10
3345 Sotaro Ito 2 2 5 1 0.10
3346 Tyler Watson 2 2 5 1 0.10
3347 Vidur D’Alia 2 2 5 1 0.10
3348 Walfried Ernst 2 2 5 1 0.10
3349 William Alexander 2 2 5 1 0.10
3350 Étienne Bruneau 2 2 5 1 0.10
3351 Xia Jiang 2 1 22 1 0.10
3352 Anthony Beck 1 1 16 7 0.10
3353 Kari Barnes 1 1 16 7 0.10
3354 Kimberly Gross 1 1 16 7 0.10
3355 Nadine Eberhardt 1 1 27 4 0.10
3356 Chao Tao 2 1 18 2 0.10
3357 Christopher Evans 1 1 23 5 0.10
3358 Emmi Biggen 1 1 23 5 0.10
3359 Johanna Hauffer 1 1 23 5 0.10
3360 Maike Trubin 1 1 23 5 0.10
3361 Carol Reid 2 2 8 0 0.10
3362 Chad Marks 2 2 8 0 0.10
3363 Erik Harris 2 2 8 0 0.10
3364 Gang Kong 2 2 8 0 0.10
3365 Jing Wu 2 2 8 0 0.10
3366 Juan Wang 2 2 8 0 0.10
3367 Liwia Piejko 2 2 8 0 0.10
3368 Nicolas Blanc 2 2 8 0 0.10
3369 Richard Rodriguez 2 2 8 0 0.10
3370 Ryohei Ito 2 2 8 0 0.10
3371 Stéphane Hernandez 2 2 8 0 0.10
3372 The Nulls 2 2 8 0 0.10
3373 Xia Xiao 2 2 8 0 0.10
3374 Yan Gu 2 2 8 0 0.10
3375 Zoé Roux 2 2 8 0 0.10
3376 Élisabeth Vasseur 2 2 8 0 0.10
3377 Adam Arnold 1 1 30 3 0.10
3378 Carlos Townsend 1 1 30 3 0.10
3379 Dustin Lopez 2 2 4 1 0.10
3380 Emily Raymond 2 2 4 1 0.10
3381 Gabriel Barr 2 2 4 1 0.10
3382 Ida Lachmann 2 2 4 1 0.10
3383 Isabell Berger 2 2 4 1 0.10
3384 Jason Brown 2 2 4 1 0.10
3385 Juan Guo 2 2 4 1 0.10
3386 Justin Thomas 2 2 4 1 0.10
3387 Kathleen Harvey 2 2 4 1 0.10
3388 Latoya English 2 2 4 1 0.10
3389 Laura Anderson 2 2 4 1 0.10
3390 Leonard Phillips 2 2 4 1 0.10
3391 Lise Gerrits 2 2 4 1 0.10
3392 Matthew Gallagher 2 2 4 1 0.10
3393 Matthew Nichols 2 2 4 1 0.10
3394 Matthew Robertson 2 2 4 1 0.10
3395 Michael French 2 2 4 1 0.10
3396 Michelle Smith 2 2 4 1 0.10
3397 Nikola Nerger 2 2 4 1 0.10
3398 Seth Duncan 2 2 4 1 0.10
3399 Siglinde Ruppersberger 2 2 4 1 0.10
3400 Walter Bell 2 2 4 1 0.10
3401 Wei Yi 2 2 4 1 0.10
3402 Yang Tan 2 2 4 1 0.10
3403 Yong Liu 2 2 4 1 0.10
3404 Brandy Reyes 1 1 26 4 0.10
3405 Dennis Wood 1 1 26 4 0.10
3406 Justin Kirk 1 1 26 4 0.10
3407 Laura Gibbs 1 1 26 4 0.10
3408 Mary Medina 1 1 26 4 0.10
3409 Pamela Miller 1 1 26 4 0.10
3410 Peter Vega 1 1 26 4 0.10
3411 Raymond Mccoy 1 1 26 4 0.10
3412 Rhonda Brown 1 1 26 4 0.10
3413 Yong Shao 2 1 17 2 0.10
3414 Gabriel Olson 1 1 22 5 0.10
3415 James Stevens 1 1 22 5 0.10
3416 Jeremy Little 1 1 22 5 0.10
3417 Yong Song 1 1 22 5 0.10
3418 Bertrand-Robert Duval 2 2 7 0 0.10
3419 Dustin Newman 2 2 7 0 0.10
3420 Edward Jacobs 2 2 7 0 0.10
3421 Eleanora Bersani 2 2 7 0 0.10
3422 Elizabeth Garcia 2 2 7 0 0.10
3423 Fang Fu 2 2 7 0 0.10
3424 Fang Hu 2 2 7 0 0.10
3425 Fang Luo 2 2 7 0 0.10
3426 Fang Yu 2 2 7 0 0.10
3427 Hector Davis 2 2 7 0 0.10
3428 Jie Meng 2 2 7 0 0.10
3429 Jing Xiao 2 2 7 0 0.10
3430 Juan Wan 2 2 7 0 0.10
3431 Kevin Burch 2 2 7 0 0.10
3432 Kimberly Moore 2 2 7 0 0.10
3433 Li Qian 2 2 7 0 0.10
3434 Li Tao 2 2 7 0 0.10
3435 Roger Orozco 2 2 7 0 0.10
3436 Tao Tao 2 2 7 0 0.10
3437 Timothy Moore 2 2 7 0 0.10
3438 William Brown 2 2 7 0 0.10
3439 Xia Wei 2 2 7 0 0.10
3440 Yong Zhou 2 2 7 0 0.10
3441 Mark Garcia 2 1 13 3 0.10
3442 Shannon Smith 2 1 13 3 0.10
3443 Adriano Tomei 2 2 3 1 0.10
3444 Aniela Wszoła 2 2 3 1 0.10
3445 Becky Walters 2 2 3 1 0.10
3446 Bernard-André Rolland 2 2 3 1 0.10
3447 Brad Martinez 2 2 3 1 0.10
3448 Brandon Hoffman 2 2 3 1 0.10
3449 Christelle Grégoire 2 2 3 1 0.10
3450 Daniel Smith 2 2 3 1 0.10
3451 Devansh Divan 2 2 3 1 0.10
3452 Elwira Heuser 2 2 3 1 0.10
3453 Fateh Dube 2 2 3 1 0.10
3454 Fiamma Baracca 2 2 3 1 0.10
3455 Gary Tucker 2 2 3 1 0.10
3456 Holly Love 2 2 3 1 0.10
3457 Isabelle Baudry de Michel 2 2 3 1 0.10
3458 Jacob Yang 2 2 3 1 0.10
3459 Jamie Schmidt 2 2 3 1 0.10
3460 Janice Schultz 2 2 3 1 0.10
3461 Jonathan Lewis 2 2 3 1 0.10
3462 Joseph Chen 2 2 3 1 0.10
3463 Joseph Ramirez 2 2 3 1 0.10
3464 Kenneth Randolph 2 2 3 1 0.10
3465 Larry Walters 2 2 3 1 0.10
3466 Laura Stanton 2 2 3 1 0.10
3467 Lodovico Interminei 2 2 3 1 0.10
3468 Macaria de Landa 2 2 3 1 0.10
3469 Mateusz Mikoda 2 2 3 1 0.10
3470 Matthew Carter 2 2 3 1 0.10
3471 Megan Morrow 2 2 3 1 0.10
3472 Misha Batta 2 2 3 1 0.10
3473 Robert Lynn 2 2 3 1 0.10
3474 Serafina Céspedes Sales 2 2 3 1 0.10
3475 Sherry Hernandez 2 2 3 1 0.10
3476 Sumer Kakar 2 2 3 1 0.10
3477 Timothy Riley 2 2 3 1 0.10
3478 Tobiasz Haponiuk 2 2 3 1 0.10
3479 Phillip Brooks 2 1 9 4 0.10
3480 Anna Glass 1 1 25 4 0.10
3481 Jamie Terry 1 1 25 4 0.10
3482 Kimberly Alvarez 1 1 25 4 0.10
3483 Nancy Macias 1 1 25 4 0.10
3484 Rebecca Robinson 1 1 25 4 0.10
3485 Samuel Thompson 1 1 25 4 0.10
3486 Nikolaos Jäkel 1 1 21 5 0.09
3487 Biju Swamy 2 2 6 0 0.09
3488 Chao Liu 2 2 6 0 0.09
3489 Chao Sun 2 2 6 0 0.09
3490 Dennis Mcdowell 2 2 6 0 0.09
3491 Elakshi Venkataraman 2 2 6 0 0.09
3492 Electric Collective 2 2 6 0 0.09
3493 Enzio Garibaldi 2 2 6 0 0.09
3494 Ethan Clarke 2 2 6 0 0.09
3495 Fang Mo 2 2 6 0 0.09
3496 Gabrielle Richardson 2 2 6 0 0.09
3497 Gang Deng 2 2 6 0 0.09
3498 Heidi Allen 2 2 6 0 0.09
3499 Jason Warner 2 2 6 0 0.09
3500 Jeffrey Watson 2 2 6 0 0.09
3501 Jing Dai 2 2 6 0 0.09
3502 Jing Dong 2 2 6 0 0.09
3503 Jing Yao 2 2 6 0 0.09
3504 Jonathan Marquez 2 2 6 0 0.09
3505 Juan Hall 2 2 6 0 0.09
3506 Jun Zeng 2 2 6 0 0.09
3507 Kara Lee 2 2 6 0 0.09
3508 Keya Kota 2 2 6 0 0.09
3509 Li Pan 2 2 6 0 0.09
3510 Lori Henderson 2 2 6 0 0.09
3511 Margaret Mccarthy 2 2 6 0 0.09
3512 Min Gu 2 2 6 0 0.09
3513 Min Tian 2 2 6 0 0.09
3514 Miss Allison King 2 2 6 0 0.09
3515 Ping Lu 2 2 6 0 0.09
3516 Qiang Tao 2 2 6 0 0.09
3517 Qiang Xia 2 2 6 0 0.09
3518 Ricciotti Traetta 2 2 6 0 0.09
3519 Shaan Jaggi 2 2 6 0 0.09
3520 Sophie Bennett 2 2 6 0 0.09
3521 Sosimo Donaire Marí 2 2 6 0 0.09
3522 Syncopated Symphony Collective 2 2 6 0 0.09
3523 Velvet Horizon 2 2 6 0 0.09
3524 Wei Zou 2 2 6 0 0.09
3525 Xiulan Qiao 2 2 6 0 0.09
3526 Xiulan Yu 2 2 6 0 0.09
3527 Xiuying Xue 2 2 6 0 0.09
3528 Yan Ma 2 2 6 0 0.09
3529 Yong Feng 2 2 6 0 0.09
3530 Minoru Nakajima 2 1 12 3 0.09
3531 Alexander Jenkins 2 2 2 1 0.09
3532 Amy Moore 2 2 2 1 0.09
3533 Angela Wallace 2 2 2 1 0.09
3534 Crossroads & Cipher 2 2 2 1 0.09
3535 Daniel Cooper 2 2 2 1 0.09
3536 Elizabeth Willis 2 2 2 1 0.09
3537 Emily Bush 2 2 2 1 0.09
3538 Eric Briggs 2 2 2 1 0.09
3539 Francisco Manning 2 2 2 1 0.09
3540 Frau Melissa Neuschäfer 2 2 2 1 0.09
3541 Gerold Mangold 2 2 2 1 0.09
3542 Jason Singleton 2 2 2 1 0.09
3543 Kimberly Rodriguez 2 2 2 1 0.09
3544 Mandy Murphy 2 2 2 1 0.09
3545 Margrit Stiffel 2 2 2 1 0.09
3546 Mary Smith 2 2 2 1 0.09
3547 Melissa Thomas 2 2 2 1 0.09
3548 Melissa Wilson 2 2 2 1 0.09
3549 Melissa Wyatt 2 2 2 1 0.09
3550 Monika Przewłoka 2 2 2 1 0.09
3551 Nikolas Naser 2 2 2 1 0.09
3552 Norbert Flisek 2 2 2 1 0.09
3553 Renato Holsten 2 2 2 1 0.09
3554 Seth Payne 2 2 2 1 0.09
3555 Stacy Hendricks 2 2 2 1 0.09
3556 Stilla Koch 2 2 2 1 0.09
3557 The Garage Dolls 2 2 2 1 0.09
3558 Walentina Ernst-Klingelhöfer 2 2 2 1 0.09
3559 Gwendolyn Fitzgerald 2 1 8 4 0.09
3560 Sharon Sanchez 2 1 19 1 0.09
3561 Xiulan Jin 2 1 19 1 0.09
3562 Christopher Gonzalez 1 1 24 4 0.09
3563 Emily Johnson 1 1 24 4 0.09
3564 Gary Torres 1 1 24 4 0.09
3565 Gregory Porter 1 1 24 4 0.09
3566 James Cain 1 1 24 4 0.09
3567 Leonard Dudley 1 1 24 4 0.09
3568 Lisa Page 1 1 24 4 0.09
3569 Patricia Johnson 1 1 24 4 0.09
3570 Thomas Mcconnell 1 1 24 4 0.09
3571 Xiuying Wen 1 1 24 4 0.09
3572 Pamela Johnson 2 1 15 2 0.09
3573 Jason Aguirre 1 1 20 5 0.09
3574 John Graves 1 1 20 5 0.09
3575 Matthew Lane 1 1 20 5 0.09
3576 Sheryl Roman 1 1 20 5 0.09
3577 Joseph Luna 3 1 6 0 0.09
3578 Sylvia Watson 3 1 6 0 0.09
3579 Aiden Harper 2 2 5 0 0.09
3580 Antonina Zaccardo 2 2 5 0 0.09
3581 Balkan Prisms 2 2 5 0 0.09
3582 Beth Gray 2 2 5 0 0.09
3583 Brandi Riley 2 2 5 0 0.09
3584 Brita Holsten 2 2 5 0 0.09
3585 Chao Zou 2 2 5 0 0.09
3586 Fang Wen 2 2 5 0 0.09
3587 Finn Morgan 2 2 5 0 0.09
3588 Frank Johnson 2 2 5 0 0.09
3589 Heinz-Gerd Klapp 2 2 5 0 0.09
3590 Highway Vibes 2 2 5 0 0.09
3591 Jessica Lewis 2 2 5 0 0.09
3592 Jing Xia 2 2 5 0 0.09
3593 Jing Zhao 2 2 5 0 0.09
3594 Johnathan Sanchez 2 2 5 0 0.09
3595 Kinship Harmony 2 2 5 0 0.09
3596 Lei Su 2 2 5 0 0.09
3597 Lidia Battisti-Righi 2 2 5 0 0.09
3598 Marianela Vila Zaragoza 2 2 5 0 0.09
3599 Megan Baird 2 2 5 0 0.09
3600 Michael Peterson 2 2 5 0 0.09
3601 Rita Finzi 2 2 5 0 0.09
3602 Romana Iadanza 2 2 5 0 0.09
3603 Skylar Brooks 2 2 5 0 0.09
3604 Steven Boyd 2 2 5 0 0.09
3605 Tao Luo 2 2 5 0 0.09
3606 Xavier Gaillard 2 2 5 0 0.09
3607 Xiulan Wan 2 2 5 0 0.09
3608 Alicia Robertson 1 1 16 6 0.09
3609 Amanda Ochoa 2 2 1 1 0.09
3610 Blake Rodriguez 2 2 1 1 0.09
3611 Bryan Sanchez 2 2 1 1 0.09
3612 Carrie Martin 2 2 1 1 0.09
3613 Charles Wallace 2 2 1 1 0.09
3614 Concetta Klotz 2 2 1 1 0.09
3615 Douglas Lewis 2 2 1 1 0.09
3616 Elizabeth Cochran PhD 2 2 1 1 0.09
3617 Evelien Becht-Versluijs 2 2 1 1 0.09
3618 Nanami Watanabe 2 2 1 1 0.09
3619 Navya Rana 2 2 1 1 0.09
3620 Ronald Brooks 2 2 1 1 0.09
3621 Sophia Dippel 2 2 1 1 0.09
3622 Susan Willis 2 2 1 1 0.09
3623 Whitney Taylor 2 2 1 1 0.09
3624 Yoichi Sato 2 2 1 1 0.09
3625 Yvonne Alexander 2 2 1 1 0.09
3626 Zeeshan Dora 2 2 1 1 0.09
3627 Éric Daniel 2 2 1 1 0.09
3628 Adrien Weiss 1 1 23 4 0.09
3629 Charles du Perez 1 1 23 4 0.09
3630 Damon Stewart 1 1 23 4 0.09
3631 Pauline Guyot de la Maillet 1 1 23 4 0.09
3632 Ryohei Sato 1 1 23 4 0.09
3633 Steve Washington 1 1 23 4 0.09
3634 James Thomas 1 1 19 5 0.09
3635 Thomas Bradley 1 1 19 5 0.09
3636 Xia Kong 1 1 19 5 0.09
3637 Baudelio Anglada-Valenzuela 2 2 4 0 0.09
3638 Charles Delaunay 2 2 4 0 0.09
3639 Elizabeth Mccarthy 2 2 4 0 0.09
3640 Hans-Josef Killer 2 2 4 0 0.09
3641 James Williams 2 2 4 0 0.09
3642 Kerri Weeks 2 2 4 0 0.09
3643 Lauren Foster 2 2 4 0 0.09
3644 Lauren Powell 2 2 4 0 0.09
3645 Louis Armstrong 2 2 4 0 0.09
3646 Marc Raymond 2 2 4 0 0.09
3647 Miloš Pokorný 2 2 4 0 0.09
3648 Mirja Jacob 2 2 4 0 0.09
3649 Mohamed Sastre Godoy 2 2 4 0 0.09
3650 N.V. Blake 2 2 4 0 0.09
3651 Prudencio Nilo Arjona Huguet 2 2 4 0 0.09
3652 Sahil Khalsa 2 2 4 0 0.09
3653 Wei Wei 2 2 4 0 0.09
3654 Knarren 2 1 10 3 0.09
3655 Angela Thompson 2 1 17 1 0.09
3656 Cynthia Nunez 2 1 17 1 0.09
3657 Veronica Ferguson 2 1 17 1 0.09
3658 Friedl Bauer-Hövel 1 1 22 4 0.09
3659 Hartmut Ruppert 1 1 22 4 0.09
3660 Heiderose Putz 1 1 22 4 0.09
3661 Herr Hans Dieter Hentschel 1 1 22 4 0.09
3662 Hertha Schinke 1 1 22 4 0.09
3663 Shray Agarwal 1 1 7 8 0.09
3664 Kimberly Estrada 1 1 18 5 0.09
3665 Édouard-Rémy Clerc 1 1 18 5 0.09
3666 Lucas Bennett 3 1 4 0 0.09
3667 Maya Torres 3 1 4 0 0.09
3668 Olivia Carter 3 1 4 0 0.09
3669 Ashley Brooks 2 2 3 0 0.09
3670 Bethany Jennings 2 2 3 0 0.09
3671 Brittany Harvey 2 2 3 0 0.09
3672 Carlos Duffy 2 2 3 0 0.09
3673 Colin Wood 2 2 3 0 0.09
3674 Elizabeth Parker 2 2 3 0 0.09
3675 Herr Folkert Vogt 2 2 3 0 0.09
3676 Ivana Vávrová 2 2 3 0 0.09
3677 Jie Chen 2 2 3 0 0.09
3678 Jun Xue 2 2 3 0 0.09
3679 Justin Berry 2 2 3 0 0.09
3680 Kathryn Mcknight 2 2 3 0 0.09
3681 Lila Rivers 2 2 3 0 0.09
3682 Mia Waters 2 2 3 0 0.09
3683 Nancy Arnold 2 2 3 0 0.09
3684 Ritvik Raman 2 2 3 0 0.09
3685 William Knapp 2 2 3 0 0.09
3686 Dawid Golda 1 1 14 6 0.09
3687 Brandi Noble 1 1 25 3 0.09
3688 Hailey Hak 1 1 25 3 0.09
3689 Lynn Brumleve-van der Horst 1 1 25 3 0.09
3690 Maud van de Pol 1 1 25 3 0.09
3691 Mohamed van de Berg 1 1 25 3 0.09
3692 Olaf Otto 1 1 25 3 0.09
3693 Otmar Fröhlich 1 1 25 3 0.09
3694 Richard Sweeney 1 1 25 3 0.09
3695 Cody Moore 2 1 16 1 0.09
3696 Jacqueline Young 2 1 16 1 0.09
3697 John Ray 2 1 16 1 0.09
3698 Kyle Hill 2 1 16 1 0.09
3699 Kevin Davidson 1 1 21 4 0.09
3700 Nicole Clark 1 1 21 4 0.09
3701 Alicia Heinz 2 1 12 2 0.09
3702 Ilona Stolze 2 1 12 2 0.09
3703 Rose-Marie Schmidtke 2 1 12 2 0.09
3704 Xiuying Wei 2 1 12 2 0.09
3705 Francisco Lyons 1 1 17 5 0.09
3706 Hector Porcel Gomis 1 1 17 5 0.09
3707 Nicolasa Zaragoza Asenjo 1 1 17 5 0.09
3708 Clara Henderson 2 2 2 0 0.09
3709 Daniel Aubert-Brunet 2 2 2 0 0.09
3710 Ethan Crowley 2 2 2 0 0.09
3711 Friedrich-Wilhelm Ullmann 2 2 2 0 0.09
3712 Inès-Adèle Valette 2 2 2 0 0.09
3713 Jing Wan 2 2 2 0 0.09
3714 Mark Beck 2 2 2 0 0.09
3715 Mary Brooks 2 2 2 0 0.09
3716 Melissa Harris 2 2 2 0 0.09
3717 Milo Knight 2 2 2 0 0.09
3718 Odette-Inès Lelièvre 2 2 2 0 0.09
3719 Renee Lynch 2 2 2 0 0.09
3720 Zara Quinn 2 2 2 0 0.09
3721 Jie Gao 1 1 24 3 0.09
3722 Kara Frazier 1 1 24 3 0.09
3723 Andrew Jenkins 2 1 15 1 0.09
3724 Matthew Salazar 2 1 15 1 0.09
3725 Antonietta Zamengo-Zanazzo 1 1 20 4 0.09
3726 Douglas Gonzalez 1 1 20 4 0.09
3727 Joseph Beltran 1 1 20 4 0.09
3728 Patrizio Antonello-Salvemini 1 1 20 4 0.09
3729 Troy Ruiz 1 1 20 4 0.09
3730 Charles Mccoy 1 1 16 5 0.09
3731 Suhana Din 1 1 16 5 0.09
3732 Anya Krishnan 2 2 1 0 0.09
3733 Denise Knight 2 2 1 0 0.09
3734 Derek Cline 2 2 1 0 0.09
3735 Edward Henry 2 2 1 0 0.09
3736 Ishita Bahri 2 2 1 0 0.09
3737 Krish Wali 2 2 1 0 0.09
3738 Melissa Warren 2 2 1 0 0.09
3739 Nirvi Chahal 2 2 1 0 0.09
3740 Pihu Gulati 2 2 1 0 0.09
3741 Siya Sahni 2 2 1 0 0.09
3742 Alan Wilson 1 1 23 3 0.09
3743 Bradley Wright 1 1 23 3 0.09
3744 Douglas Turner 1 1 23 3 0.09
3745 Gregory Wright 1 1 23 3 0.09
3746 Stephen Ramirez 1 1 23 3 0.09
3747 Cheryl Suarez 2 1 14 1 0.09
3748 Jacek Gorol 1 1 8 7 0.09
3749 Tadeusz Majorek 1 1 8 7 0.09
3750 Geneviève Duhamel 1 1 19 4 0.09
3751 Johan Hermann 1 1 19 4 0.09
3752 Katie Hartman 1 1 19 4 0.09
3753 Magnus Geisler 1 1 19 4 0.09
3754 Matthew Good 1 1 19 4 0.09
3755 Michel Dippel 1 1 19 4 0.09
3756 Brigitte Gotthard 2 1 10 2 0.08
3757 Yong Yang 2 1 10 2 0.08
3758 Bryan Davis 1 1 15 5 0.08
3759 Gina Cox 1 1 15 5 0.08
3760 Sherry Martin 1 1 15 5 0.08
3761 Rigo Becker 2 2 0 0 0.08
3762 Curtis Underwood 1 1 22 3 0.08
3763 Franklin Allen 1 1 22 3 0.08
3764 Jolanta Zirme 1 1 22 3 0.08
3765 Siegmund Winkler 1 1 22 3 0.08
3766 Wendy Porter 1 1 22 3 0.08
3767 Brandon Wilson 2 1 13 1 0.08
3768 Jeanne Miller 2 1 13 1 0.08
3769 Wei Zhong 2 1 13 1 0.08
3770 Sandra Majak 1 1 7 7 0.08
3771 Alexandra Obrien 1 1 18 4 0.08
3772 Carmine Harloff 1 1 18 4 0.08
3773 Cynthia Reed 1 1 18 4 0.08
3774 Ida Stolze-Lübs 1 1 18 4 0.08
3775 Juan Zhao 1 1 18 4 0.08
3776 Kerry Henry 1 1 18 4 0.08
3777 Marcus Berry 1 1 18 4 0.08
3778 Michele Sharp 1 1 18 4 0.08
3779 Gang Xiong 2 1 9 2 0.08
3780 Karina Gunpf 2 1 9 2 0.08
3781 Jun Hou 1 1 14 5 0.08
3782 Steven Ray 1 1 14 5 0.08
3783 Jessica Lindsey 1 1 25 2 0.08
3784 Chao Su 1 1 21 3 0.08
3785 Christopher Leonard 1 1 21 3 0.08
3786 Ethan Sanchez 1 1 21 3 0.08
3787 Tao Hao 1 1 21 3 0.08
3788 Jun Lai 2 1 12 1 0.08
3789 Arthur Sparks 1 1 17 4 0.08
3790 Betti Löffler 1 1 17 4 0.08
3791 Florence Martin 1 1 17 4 0.08
3792 Ida Loos 1 1 17 4 0.08
3793 Krystal Martin 1 1 17 4 0.08
3794 Laura Hernandez 1 0 23 7 0.08
3795 Zachary Stanley 1 0 23 7 0.08
3796 Collective Conscience 2 1 8 2 0.08
3797 Melissa Smith 2 1 15 0 0.08
3798 Anna Gonzales 1 1 20 3 0.08
3799 Gerhild Schweitzer 1 1 20 3 0.08
3800 Hasso Kreusel 1 1 20 3 0.08
3801 Jasmine Moore 1 1 20 3 0.08
3802 Joy Wood 1 1 20 3 0.08
3803 Mathilde Hesse 1 1 20 3 0.08
3804 Qiang Liao 1 1 20 3 0.08
3805 Tina Watson 1 1 20 3 0.08
3806 Yan Zheng 1 1 20 3 0.08
3807 Bas Jansen 1 1 16 4 0.08
3808 Hailey Timmerman-van der Berg 1 1 16 4 0.08
3809 Lily Sprong 1 1 16 4 0.08
3810 Sherri Mclaughlin 1 1 12 5 0.08
3811 Kendra Walsh 1 1 23 2 0.08
3812 Kyle Reynolds 1 1 23 2 0.08
3813 Nicholas Patel 1 1 23 2 0.08
3814 Alan Mcgee 1 1 19 3 0.08
3815 Amanda Hunter 1 1 19 3 0.08
3816 Antonio Fuentes 1 1 19 3 0.08
3817 Arnaude Poirier 1 1 19 3 0.08
3818 Brenda Anderson 1 1 19 3 0.08
3819 Cheryl Martinez 1 1 19 3 0.08
3820 Christophe-Émile Grégoire 1 1 19 3 0.08
3821 Georges Chrétien 1 1 19 3 0.08
3822 Jaime Watkins 1 1 19 3 0.08
3823 Jonathan Avila 1 1 19 3 0.08
3824 Lisa Shaw 1 1 19 3 0.08
3825 Lori Williams 1 1 19 3 0.08
3826 Luce Loiseau 1 1 19 3 0.08
3827 Mark Barr 1 1 19 3 0.08
3828 Mark Thomas 1 1 19 3 0.08
3829 Min Gong 1 1 19 3 0.08
3830 Nicolas Gosselin du Robert 1 1 19 3 0.08
3831 Tracy Bennett 1 1 19 3 0.08
3832 William Carter 1 1 19 3 0.08
3833 Jing Huang 2 1 10 1 0.08
3834 Yan Sun 2 1 10 1 0.08
3835 Alan Clark 1 1 15 4 0.08
3836 Andrew Martin 1 1 15 4 0.08
3837 Emily Drake 1 1 15 4 0.08
3838 Tamara Miller 1 1 15 4 0.08
3839 Greca Matteotti-Jovinelli 1 1 11 5 0.08
3840 Michelotto Toldo 1 1 11 5 0.08
3841 April Anderson 1 1 22 2 0.08
3842 Dana Walters 1 1 22 2 0.08
3843 Daniel Edwards 1 1 22 2 0.08
3844 Deborah Brown 1 1 22 2 0.08
3845 Derek Cook 1 1 22 2 0.08
3846 Elizabeth West 1 1 22 2 0.08
3847 Jared Arnold 1 1 22 2 0.08
3848 Jeremy Macias 1 1 22 2 0.08
3849 Jonathan Garcia 1 1 22 2 0.08
3850 Ralph Johnston 1 1 22 2 0.08
3851 Stacy Bryant 1 1 22 2 0.08
3852 Tanya Evans 1 1 22 2 0.08
3853 Todd Jones 1 1 22 2 0.08
3854 Victoria Butler 1 1 22 2 0.08
3855 Darlene Watson 1 1 18 3 0.08
3856 Darryl Curtis 1 1 18 3 0.08
3857 Janette Schleich 1 1 18 3 0.08
3858 Joseph Reyes 1 1 18 3 0.08
3859 Kevin Pruitt 1 1 18 3 0.08
3860 Marcel Kreusel 1 1 18 3 0.08
3861 Maria Powell 1 1 18 3 0.08
3862 Mikayla Russell 1 1 18 3 0.08
3863 Ralf Budig 1 1 18 3 0.08
3864 Carrie Cobb 1 1 14 4 0.08
3865 Gilles Raymond 1 1 14 4 0.08
3866 Justin Sullivan 1 1 14 4 0.08
3867 Kathleen Carroll 1 1 14 4 0.08
3868 Kenneth Knight 1 1 14 4 0.08
3869 Tammy Higgins 1 1 14 4 0.08
3870 Tiffany Lutz 1 1 14 4 0.08
3871 Nathaniel Craig 2 1 5 2 0.08
3872 Denise Barthelemy de Brunet 1 1 21 2 0.08
3873 Dina Scholten 1 1 21 2 0.08
3874 Jamie Garcia 1 1 21 2 0.08
3875 Kornelia Michnik 1 1 21 2 0.08
3876 Kristopher Roberts 1 1 21 2 0.08
3877 Na Qiao 1 1 21 2 0.08
3878 Nathalie Beer-Huhn 1 1 21 2 0.08
3879 Nicola Strangio 1 1 21 2 0.08
3880 Nicole Murphy 1 1 21 2 0.08
3881 Roberto Govoni 1 1 21 2 0.08
3882 Sherry Salinas 1 1 21 2 0.08
3883 Stephanie Murray 1 1 21 2 0.08
3884 Annunziata Trezzini 1 1 17 3 0.08
3885 Donna Vecellio 1 1 17 3 0.08
3886 Fulvio Comisso 1 1 17 3 0.08
3887 Jim Miller 1 1 17 3 0.08
3888 Monica Thompson 1 1 17 3 0.08
3889 Ping Xiao 1 1 17 3 0.08
3890 Sophia Cilea 1 1 17 3 0.08
3891 Steven Goodman 1 1 17 3 0.08
3892 Alexandra Taylor 1 1 13 4 0.08
3893 Baudelio Naranjo Ríos 1 1 13 4 0.08
3894 Birger Gutknecht 1 1 13 4 0.08
3895 Cameron Cohen 1 1 13 4 0.08
3896 Charvi Bains 1 1 13 4 0.08
3897 Haley Campbell 1 1 13 4 0.08
3898 Heinz-Günter Schmiedecke 1 1 13 4 0.08
3899 Holm Segebahn 1 1 13 4 0.08
3900 Hrishita Sura 1 1 13 4 0.08
3901 Ibrahim Heerkens-Everde 1 1 13 4 0.08
3902 Klaus-Werner Gerlach 1 1 13 4 0.08
3903 Megan Bowers 1 1 13 4 0.08
3904 Paca Ema Mosquera Cepeda 1 1 13 4 0.08
3905 Plinio Ribas 1 1 13 4 0.08
3906 Robby Schmiedecke 1 1 13 4 0.08
3907 Rocío Rovira 1 1 13 4 0.08
3908 Ulises Gomila Morell 1 1 13 4 0.08
3909 Valerie Levine 1 1 13 4 0.08
3910 Xiuying Yang 1 1 13 4 0.08
3911 Astrid Jockel-Hecker 1 1 24 1 0.07
3912 Denis Pechel 1 1 24 1 0.07
3913 Eckehard Kabus-Barth 1 1 24 1 0.07
3914 Elias Beyer 1 1 24 1 0.07
3915 Erhard Blümel 1 1 24 1 0.07
3916 Ernst-August Hamann-Junck 1 1 24 1 0.07
3917 Franco Dietz 1 1 24 1 0.07
3918 Gerfried Schaaf 1 1 24 1 0.07
3919 Inna Wagner 1 1 24 1 0.07
3920 Irmtrud Jessel 1 1 24 1 0.07
3921 Isabelle Bloch 1 1 24 1 0.07
3922 Ivonne Austermühle 1 1 24 1 0.07
3923 Juan Brown 1 1 24 1 0.07
3924 Margarete Löchel 1 1 24 1 0.07
3925 Mirja Heintze 1 1 24 1 0.07
3926 Pawel Zirme 1 1 24 1 0.07
3927 Sarah Miles 1 1 24 1 0.07
3928 Sevim Holt 1 1 24 1 0.07
3929 Sieghard Jäntsch-Kensy 1 1 24 1 0.07
3930 Jo Baker 1 1 9 5 0.07
3931 Brandon Griffith 1 1 20 2 0.07
3932 Dominik Preiß 1 1 20 2 0.07
3933 Ping Liu 1 1 20 2 0.07
3934 Jun Liao 2 1 11 0 0.07
3935 Adira Chaudhuri 1 1 5 6 0.07
3936 Bart Wensen-Kof 1 1 16 3 0.07
3937 Danny Fitzgerald 1 1 16 3 0.07
3938 Eric Hopkins 1 1 16 3 0.07
3939 Kristina Sanchez 1 1 16 3 0.07
3940 Lieve van Riet 1 1 16 3 0.07
3941 Lizz van Santen 1 1 16 3 0.07
3942 Mary Jacobs 1 1 16 3 0.07
3943 Melissa Rush 1 1 16 3 0.07
3944 Min Yang 1 1 16 3 0.07
3945 Nina Mallien 1 1 16 3 0.07
3946 Ryan Mcguire 1 1 16 3 0.07
3947 Stephen Allen 1 1 16 3 0.07
3948 Tanya Ratta 1 1 16 3 0.07
3949 Ulf Kusch 2 1 7 1 0.07
3950 Gitta Wilmsen 1 1 12 4 0.07
3951 Larry Mendez 1 1 12 4 0.07
3952 Lisa Crawford 2 1 3 2 0.07
3953 Kimberly Dominguez 1 1 8 5 0.07
3954 Ricky Franklin 1 1 8 5 0.07
3955 Alexander Carr 1 1 19 2 0.07
3956 Amanda Peterson 1 1 19 2 0.07
3957 Baptist Gierschner 1 1 19 2 0.07
3958 Colleen Richards 1 1 19 2 0.07
3959 Elizabeth Bradshaw 1 1 19 2 0.07
3960 Joseph Ho 1 1 19 2 0.07
3961 Madeleine Wulf 1 1 19 2 0.07
3962 Rosalie Hörle 1 1 19 2 0.07
3963 Savannah Shaw 1 1 19 2 0.07
3964 Anthony Smith 2 1 10 0 0.07
3965 Guiying Qian 2 1 10 0 0.07
3966 Qiang Duan 2 1 10 0 0.07
3967 Jing Xu 1 1 15 3 0.07
3968 Kai-Uwe Döring 1 1 15 3 0.07
3969 Robert Allen 1 1 15 3 0.07
3970 Yoko Murakami 1 1 15 3 0.07
3971 Annunziata Bertoli-Ruberto 2 1 6 1 0.07
3972 Antoine du Adam 2 1 6 1 0.07
3973 Augustin Raymond 2 1 6 1 0.07
3974 Chantal Gérard 2 1 6 1 0.07
3975 Claudine Leleu 2 1 6 1 0.07
3976 Julien Collin-Texier 2 1 6 1 0.07
3977 Min Han 2 1 6 1 0.07
3978 Sylvie-Chantal Schmitt 2 1 6 1 0.07
3979 Eva Sani 1 1 11 4 0.07
3980 Lia Wähner-Weimer 1 1 11 4 0.07
3981 Stacy Hudson 1 1 11 4 0.07
3982 Steven Carter 1 1 11 4 0.07
3983 Feliciano Noguera-Vallejo 2 1 2 2 0.07
3984 Natasza Sygut 1 1 7 5 0.07
3985 Pamela Graham 1 1 7 5 0.07
3986 Aaron Conway 1 1 18 2 0.07
3987 Adam Hanna 1 1 18 2 0.07
3988 Alexandria Spencer 1 1 18 2 0.07
3989 Allison Johnson 1 1 18 2 0.07
3990 Amber Franklin 1 1 18 2 0.07
3991 Chris Meyer 1 1 18 2 0.07
3992 Fang Shao 1 1 18 2 0.07
3993 Jody Lopez 1 1 18 2 0.07
3994 Leslie Wagner 1 1 18 2 0.07
3995 Marie Flores 1 1 18 2 0.07
3996 Ming Peng 1 1 18 2 0.07
3997 Molly Williams 1 1 18 2 0.07
3998 Sabrina Mcmahon 1 1 18 2 0.07
3999 William Stone 1 1 18 2 0.07
4000 George Jones 2 1 9 0 0.07
4001 Ming Deng 2 1 9 0 0.07
4002 Nicole Smith 2 1 9 0 0.07
4003 Anaya Randhawa 1 1 14 3 0.07
4004 Anvi Chawla 1 1 14 3 0.07
4005 Steven Green 1 1 14 3 0.07
4006 Yuvaan Tripathi 1 1 14 3 0.07
4007 Carla Gross 2 1 5 1 0.07
4008 Craig Aguilar 1 1 10 4 0.07
4009 Jon Peters 1 1 10 4 0.07
4010 Jeffery Williams 1 1 21 1 0.07
4011 Šimon Urban 1 1 21 1 0.07
4012 Devansh Seshadri 1 1 6 5 0.07
4013 Alexander Whitney 1 1 17 2 0.07
4014 Angela Porter 1 1 17 2 0.07
4015 Edwin Binner 1 1 17 2 0.07
4016 Evelyn Gordon 1 1 17 2 0.07
4017 Gang Wang 1 1 17 2 0.07
4018 Gernot Karz 1 1 17 2 0.07
4019 Gregory White 1 1 17 2 0.07
4020 Hansjoachim Sager 1 1 17 2 0.07
4021 Ian Jüttner 1 1 17 2 0.07
4022 James Maldonado 1 1 17 2 0.07
4023 Joseph Parsons 1 1 17 2 0.07
4024 Joshua Anderson 1 1 17 2 0.07
4025 Lori Gregory 1 1 17 2 0.07
4026 Mary Cisneros 1 1 17 2 0.07
4027 Matthew Fields 1 1 17 2 0.07
4028 Richard Frazier 1 1 17 2 0.07
4029 Rodney Clark 1 1 17 2 0.07
4030 Sonia Weeks 1 1 17 2 0.07
4031 Vanessa Clark 1 1 17 2 0.07
4032 Yang Shao 2 1 8 0 0.07
4033 Anaïs Peron 1 1 13 3 0.07
4034 Caleb Williamson 1 1 13 3 0.07
4035 Chad Green 1 1 13 3 0.07
4036 Diana Lledó Colomer 1 1 13 3 0.07
4037 Donald Bradley 1 1 13 3 0.07
4038 Jaime Arrieta 1 1 13 3 0.07
4039 Justin Wyatt 1 1 13 3 0.07
4040 Katie Johnston 1 1 13 3 0.07
4041 Livio Brenna 1 1 13 3 0.07
4042 Mai Yamada 1 1 13 3 0.07
4043 Margaret Woods 1 1 13 3 0.07
4044 Nino Pasolini 1 1 13 3 0.07
4045 Claire Garner 2 1 4 1 0.07
4046 Lauren Thomas 2 1 4 1 0.07
4047 Sheryl Massey 2 1 4 1 0.07
4048 Jacob Hunter 1 1 9 4 0.07
4049 Arkadiusz Ciesielka 1 1 5 5 0.07
4050 Adrien Boulay 1 1 16 2 0.07
4051 Alphonse-Jacques Ledoux 1 1 16 2 0.07
4052 Andre Scott 1 1 16 2 0.07
4053 Anna Mendez 1 1 16 2 0.07
4054 Annette Mckee 1 1 16 2 0.07
4055 April Morris 1 1 16 2 0.07
4056 Arthur Ollivier 1 1 16 2 0.07
4057 Athanasios Hamann 1 1 16 2 0.07
4058 Brianna Wilson 1 1 16 2 0.07
4059 Christopher Harris 1 1 16 2 0.07
4060 Eric Bartlett 1 1 16 2 0.07
4061 Gabriel Ferreira-Gillet 1 1 16 2 0.07
4062 James Harrington 1 1 16 2 0.07
4063 John Barnes 1 1 16 2 0.07
4064 Kelly Cummings 1 1 16 2 0.07
4065 Leah Farrell 1 1 16 2 0.07
4066 Lori Bass 1 1 16 2 0.07
4067 Lucie Prévost du Rousset 1 1 16 2 0.07
4068 Michael Carroll 1 1 16 2 0.07
4069 Michelle Garrett 1 1 16 2 0.07
4070 Milo Badoer 1 1 16 2 0.07
4071 Misty Reed 1 1 16 2 0.07
4072 Mitchell Hernandez 1 1 16 2 0.07
4073 Monica Hale 1 1 16 2 0.07
4074 Robert Adams 1 1 16 2 0.07
4075 Roderich Mohaupt 1 1 16 2 0.07
4076 Sara Shannon 1 1 16 2 0.07
4077 Steve Yu 1 1 16 2 0.07
4078 Susan Oneal 1 1 16 2 0.07
4079 Teresa White 1 1 16 2 0.07
4080 Thomas Beard 1 1 16 2 0.07
4081 Tiffany Lee 1 1 16 2 0.07
4082 Tonya Gallegos 1 1 16 2 0.07
4083 Jie Hao 2 1 7 0 0.07
4084 Kenneth Jones 2 1 7 0 0.07
4085 Kanav Sachar 1 1 12 3 0.07
4086 Melinda Austin 1 1 12 3 0.07
4087 Nayantara Bhatti 1 1 12 3 0.07
4088 Shray Kala 1 1 12 3 0.07
4089 Chad Garcia 2 1 3 1 0.07
4090 Donato Mocenigo 2 1 3 1 0.07
4091 Michael Snow 2 0 20 1 0.07
4092 Klara Kuźnia 1 1 8 4 0.07
4093 Arthur Miller 1 1 19 1 0.07
4094 Joshua Thomas 1 1 19 1 0.07
4095 Kathleen Bean 1 1 19 1 0.07
4096 Luis King 1 1 19 1 0.07
4097 Peggy West 1 1 19 1 0.07
4098 Sydney Gonzalez 1 1 19 1 0.07
4099 Felix Hornich 2 0 16 2 0.07
4100 Alex Ramsey 1 1 15 2 0.07
4101 Angela Kelly 1 1 15 2 0.07
4102 Ashley Larson 1 1 15 2 0.07
4103 Ashley Murphy 1 1 15 2 0.07
4104 Beatrix Schmidt 1 1 15 2 0.07
4105 Beth Thompson 1 1 15 2 0.07
4106 Bjorn van Berkum-Freshour 1 1 15 2 0.07
4107 Brian Moore 1 1 15 2 0.07
4108 Carmina Esteve Coloma 1 1 15 2 0.07
4109 Christina Garrett 1 1 15 2 0.07
4110 Daan VI-Cadefau 1 1 15 2 0.07
4111 Fang Yang 1 1 15 2 0.07
4112 Gary Chapman 1 1 15 2 0.07
4113 Hans-Karl Hövel 1 1 15 2 0.07
4114 Isabelle Rijn-Höning 1 1 15 2 0.07
4115 Jake Marchal 1 1 15 2 0.07
4116 Jeffrey Tate 1 1 15 2 0.07
4117 John Gallagher 1 1 15 2 0.07
4118 John Price 1 1 15 2 0.07
4119 Joshua Crawford 1 1 15 2 0.07
4120 Kathleen Martinez 1 1 15 2 0.07
4121 Kenneth West 1 1 15 2 0.07
4122 Larry Thomas 1 1 15 2 0.07
4123 Laura Guerrero Cañete 1 1 15 2 0.07
4124 Laura Lindsey 1 1 15 2 0.07
4125 Marina Stiebitz 1 1 15 2 0.07
4126 Melinda Vasquez 1 1 15 2 0.07
4127 Michael Kelly 1 1 15 2 0.07
4128 Nancy Gonzalez 1 1 15 2 0.07
4129 Olaf Schmiedt 1 1 15 2 0.07
4130 Pim de Roo 1 1 15 2 0.07
4131 Priska Hölzenbecher 1 1 15 2 0.07
4132 Qiang Ma 1 1 15 2 0.07
4133 Rania Banik 1 1 15 2 0.07
4134 Riya Ahluwalia 1 1 15 2 0.07
4135 Robert Rodriguez 1 1 15 2 0.07
4136 Ryan Reynolds 1 1 15 2 0.07
4137 Sarah Lee 1 1 15 2 0.07
4138 Sarah Young 1 1 15 2 0.07
4139 Stephen Ross 1 1 15 2 0.07
4140 Valerie Phillips 1 1 15 2 0.07
4141 Wayne Perkins 1 1 15 2 0.07
4142 William Duncan 1 1 15 2 0.07
4143 Yasuhiro Sato 1 1 15 2 0.07
4144 Borderlands 2 1 6 0 0.07
4145 Yong Ye 2 1 6 0 0.07
4146 Armaan Bera 1 1 11 3 0.07
4147 Benjamin Griffin 1 1 11 3 0.07
4148 Henry Scott 1 1 11 3 0.07
4149 Herr Ilhan Mülichen 1 1 11 3 0.07
4150 Gustaw Reich 1 1 7 4 0.06
4151 Jerzy Bochnak 1 1 7 4 0.06
4152 Leon Misiurek 1 1 7 4 0.06
4153 Marianna Polasik 1 1 7 4 0.06
4154 John Schmitt 1 1 18 1 0.06
4155 Kevin Parker 1 1 18 1 0.06
4156 Adam Christian 1 1 14 2 0.06
4157 Caitlin Miller 1 1 14 2 0.06
4158 Catherine Brooks 1 1 14 2 0.06
4159 Christopher Robbins 1 1 14 2 0.06
4160 Gang Liang 1 1 14 2 0.06
4161 Herbert Mcfarland 1 1 14 2 0.06
4162 Joseph Ponce 1 1 14 2 0.06
4163 Kevin King 1 1 14 2 0.06
4164 Laura Mejia 1 1 14 2 0.06
4165 Matthew Lara 1 1 14 2 0.06
4166 Megan Harris 1 1 14 2 0.06
4167 Ming Liao 1 1 14 2 0.06
4168 Nicholas Harris 1 1 14 2 0.06
4169 Rachel Montes 1 1 14 2 0.06
4170 Rebecca Wise 1 1 14 2 0.06
4171 Rosalinde Tintzmann 1 1 14 2 0.06
4172 Sean James 1 1 14 2 0.06
4173 Sebastian Vega Saldaña 1 1 14 2 0.06
4174 Stacey Martin 1 1 14 2 0.06
4175 Xiuying Qiao 1 1 14 2 0.06
4176 Yong Qin 1 1 14 2 0.06
4177 Elizabeth Gonzalez 2 1 5 0 0.06
4178 Katelyn Flores 2 1 5 0 0.06
4179 Nicole Miller 2 1 5 0 0.06
4180 Aradhya Dara 1 1 10 3 0.06
4181 Armaan Dalal 1 1 10 3 0.06
4182 Brian Mclaughlin 1 1 10 3 0.06
4183 Devin Berger 1 1 10 3 0.06
4184 Drishya Srinivasan 1 1 10 3 0.06
4185 Garrett Montoya 1 1 10 3 0.06
4186 Henry Gross 1 1 10 3 0.06
4187 Joanna Avery 1 1 10 3 0.06
4188 John Rodriguez 1 1 10 3 0.06
4189 Judy Yang 1 1 10 3 0.06
4190 Marcus Stewart 1 1 10 3 0.06
4191 Miss Jennifer Williams 1 1 10 3 0.06
4192 Molly Gonzalez 1 1 10 3 0.06
4193 William Robinson 1 1 10 3 0.06
4194 Jonathan Guerrero 2 1 1 1 0.06
4195 Autumn Johnson 1 1 17 1 0.06
4196 Craig Campbell 1 1 17 1 0.06
4197 Lauren Rich 1 1 17 1 0.06
4198 Li Peng 1 1 17 1 0.06
4199 Ramona Mezzetta 1 1 17 1 0.06
4200 Sydney James 1 1 17 1 0.06
4201 Alexis Dixon 1 1 13 2 0.06
4202 Alicia Koch 1 1 13 2 0.06
4203 Alina Leblanc 1 1 13 2 0.06
4204 Ana Sager 1 1 13 2 0.06
4205 Andrea Meyer 1 1 13 2 0.06
4206 Ann Schmidt 1 1 13 2 0.06
4207 Anthony Scott 1 1 13 2 0.06
4208 Audrey Gray 1 1 13 2 0.06
4209 Austin Perez 1 1 13 2 0.06
4210 Chad Gonzales 1 1 13 2 0.06
4211 Chao Gu 1 1 13 2 0.06
4212 Christoph Hermann 1 1 13 2 0.06
4213 Christopher Hughes 1 1 13 2 0.06
4214 Constance Meyer 1 1 13 2 0.06
4215 David Nash 1 1 13 2 0.06
4216 Debra Curry 1 1 13 2 0.06
4217 Denise Poole 1 1 13 2 0.06
4218 Diane Robbins 1 1 13 2 0.06
4219 Fang Liao 1 1 13 2 0.06
4220 Fatma Henck-Weinhold 1 1 13 2 0.06
4221 Godofredo Franch Sanabria 1 1 13 2 0.06
4222 Herr Henry Grein Groth 1 1 13 2 0.06
4223 James Murray 1 1 13 2 0.06
4224 Jessica Beck 1 1 13 2 0.06
4225 Jose Harmon 1 1 13 2 0.06
4226 Kaori Kobayashi 1 1 13 2 0.06
4227 Kenneth Miller 1 1 13 2 0.06
4228 Lei Mao 1 1 13 2 0.06
4229 Lindsey Terry 1 1 13 2 0.06
4230 Lisa Ramos 1 1 13 2 0.06
4231 Lucas Hurley 1 1 13 2 0.06
4232 Luigina Lattuada-Carfagna 1 1 13 2 0.06
4233 Marilyn Austin 1 1 13 2 0.06
4234 Marina Giradello-Pellegrini 1 1 13 2 0.06
4235 Matilda Gottardi 1 1 13 2 0.06
4236 Raffaele Cantimori-Nicolini 1 1 13 2 0.06
4237 Raymond de la Lebrun 1 1 13 2 0.06
4238 Roswita Weinhold 1 1 13 2 0.06
4239 Stefani Magrassi 1 1 13 2 0.06
4240 Tiziano Bianchi 1 1 13 2 0.06
4241 Vincenza Tropea 1 1 13 2 0.06
4242 Wei Lai 1 1 13 2 0.06
4243 Xia Liu 1 1 13 2 0.06
4244 Yui Kondo 1 1 13 2 0.06
4245 Zachary Estes 1 1 13 2 0.06
4246 Zachary Woods 1 1 13 2 0.06
4247 Aria Stone 2 1 4 0 0.06
4248 Ash Mercer 2 1 4 0 0.06
4249 Audrey Huff 2 1 4 0 0.06
4250 Green Haze Collective 2 1 4 0 0.06
4251 Jax Holloway 2 1 4 0 0.06
4252 Jaxon Rivers 2 1 4 0 0.06
4253 Lila Green 2 1 4 0 0.06
4254 Riley Vex 2 1 4 0 0.06
4255 Theo Gray 2 1 4 0 0.06
4256 Bartek Krzysiek 1 1 9 3 0.06
4257 Charles Brown 1 1 9 3 0.06
4258 Franck Gillet 1 1 9 3 0.06
4259 Gertrude Dietz-Drub 1 1 9 3 0.06
4260 Gokul Konda 1 1 9 3 0.06
4261 Hanna Aelftrud van Wessex 1 1 9 3 0.06
4262 Imran de Bruijn-Hendrikse 1 1 9 3 0.06
4263 Krista Fritsch 1 1 9 3 0.06
4264 Michael Hart 1 1 9 3 0.06
4265 Michelle Moore 1 1 9 3 0.06
4266 Prerak Keer 1 1 9 3 0.06
4267 Rocco Hauffer 1 1 9 3 0.06
4268 Thilo Sölzer 1 1 9 3 0.06
4269 Xiulan Xiong 1 1 9 3 0.06
4270 Kimberly Weaver 1 1 5 4 0.06
4271 Alan Mcclain 1 1 16 1 0.06
4272 Andrew Medina 1 1 16 1 0.06
4273 Ashley Williams 1 1 16 1 0.06
4274 Brianna Logan 1 1 16 1 0.06
4275 Brianna Tucker 1 1 16 1 0.06
4276 Christina Berry 1 1 16 1 0.06
4277 Debra Bates 1 1 16 1 0.06
4278 Franca Wilms 1 1 16 1 0.06
4279 Haley Walls 1 1 16 1 0.06
4280 Jake Casey 1 1 16 1 0.06
4281 Jeffery Koch 1 1 16 1 0.06
4282 Joseph Crawford 1 1 16 1 0.06
4283 Justin Mcclain 1 1 16 1 0.06
4284 Kelly Sweeney 1 1 16 1 0.06
4285 Matthew Hickman 1 1 16 1 0.06
4286 Melanie Barker 1 1 16 1 0.06
4287 Nicole Gray 1 1 16 1 0.06
4288 Rebecca Mcneil 1 1 16 1 0.06
4289 Richard Richards 1 1 16 1 0.06
4290 Rodney Hurst 1 1 16 1 0.06
4291 Zachary Stark 1 1 16 1 0.06
4292 Adrienne Bouchet 1 1 12 2 0.06
4293 Akira Saito 1 1 12 2 0.06
4294 Alex Moore 1 1 12 2 0.06
4295 Alfons Rudolph-Holzapfel 1 1 12 2 0.06
4296 Amanda Brown 1 1 12 2 0.06
4297 Amanda Sullivan 1 1 12 2 0.06
4298 Anthony Allen 1 1 12 2 0.06
4299 Brandon Kirby 1 1 12 2 0.06
4300 Brian Carter 1 1 12 2 0.06
4301 Brittany Moreno 1 1 12 2 0.06
4302 Caroline Navarro 1 1 12 2 0.06
4303 Christine Martin 1 1 12 2 0.06
4304 Clemens Henck 1 1 12 2 0.06
4305 Cécile Meyer 1 1 12 2 0.06
4306 Diether Lachmann 1 1 12 2 0.06
4307 Edward Clark 1 1 12 2 0.06
4308 Ernestine Killer 1 1 12 2 0.06
4309 Francesco Lachmann-Ruppert 1 1 12 2 0.06
4310 Frédérique Traore 1 1 12 2 0.06
4311 Grant Coleman 1 1 12 2 0.06
4312 Gregory Frost 1 1 12 2 0.06
4313 Hans-J. Hendriks 1 1 12 2 0.06
4314 Herr Robin Drewes 1 1 12 2 0.06
4315 Isaac Lamb 1 1 12 2 0.06
4316 James Rodriguez 1 1 12 2 0.06
4317 James Smith 1 1 12 2 0.06
4318 John Shaw 1 1 12 2 0.06
4319 Johnny Walker 1 1 12 2 0.06
4320 Juan Liao 1 1 12 2 0.06
4321 Kenneth Lee 1 1 12 2 0.06
4322 Kristin Sullivan 1 1 12 2 0.06
4323 Laura Mann 1 1 12 2 0.06
4324 Lei Yin 1 1 12 2 0.06
4325 Lioba Vollbrecht 1 1 12 2 0.06
4326 Lisa Peterson 1 1 12 2 0.06
4327 Luciano Chindamo 1 1 12 2 0.06
4328 Marlene Juncken 1 1 12 2 0.06
4329 Mikako Kondo 1 1 12 2 0.06
4330 Miss Brandi Torres 1 1 12 2 0.06
4331 Momoko Yamamoto 1 1 12 2 0.06
4332 Osamu Hayashi 1 1 12 2 0.06
4333 Richard Reyes 1 1 12 2 0.06
4334 Robert Boone 1 1 12 2 0.06
4335 Ruth Hernandez 1 1 12 2 0.06
4336 Sandra Burke 1 1 12 2 0.06
4337 Sarah Rivera 1 1 12 2 0.06
4338 Satomi Aoki 1 1 12 2 0.06
4339 Shelby Campbell 1 1 12 2 0.06
4340 Stacey Armstrong 1 1 12 2 0.06
4341 Steve Young 1 1 12 2 0.06
4342 Tao Lu 1 1 12 2 0.06
4343 Trevor Bass 1 1 12 2 0.06
4344 Viktor Kreusel 1 1 12 2 0.06
4345 William Miller 1 1 12 2 0.06
4346 Xia Cheng 1 1 12 2 0.06
4347 Yosuke Ito 1 1 12 2 0.06
4348 Iris Moon 2 1 3 0 0.06
4349 Luna Skye 2 1 3 0 0.06
4350 Max Wilder 2 1 3 0 0.06
4351 Nova Blaze 2 1 3 0 0.06
4352 Zane Cruz 2 1 3 0 0.06
4353 Adam Knox 1 1 8 3 0.06
4354 Anaya Butala 1 1 8 3 0.06
4355 Caleb Payne 1 1 8 3 0.06
4356 Christy Hernandez 1 1 8 3 0.06
4357 Elizabeth Dobes 1 1 8 3 0.06
4358 Igor Rychcik 1 1 8 3 0.06
4359 Jeffrey Ward 1 1 8 3 0.06
4360 Jeremy Alvarez 1 1 8 3 0.06
4361 John Campbell 1 1 8 3 0.06
4362 Joseph Long 1 1 8 3 0.06
4363 Lunar Prism 1 1 8 3 0.06
4364 Erik Alvarez 1 1 19 0 0.06
4365 Gary White 1 1 19 0 0.06
4366 Joel Valentine 1 1 19 0 0.06
4367 Joseph Buchanan 1 1 19 0 0.06
4368 Julie Graves 1 1 19 0 0.06
4369 Karen Smith 1 1 19 0 0.06
4370 Mario Weeks 1 1 19 0 0.06
4371 Mary Ross 1 1 19 0 0.06
4372 Nicholas Ortiz 1 1 19 0 0.06
4373 Nicole Williams 1 1 19 0 0.06
4374 Patrick Thompson 1 1 19 0 0.06
4375 Robert Holland 1 1 19 0 0.06
4376 Shawn Soto 1 1 19 0 0.06
4377 Xavier Kane 1 1 19 0 0.06
4378 Amanda Byrd 1 1 15 1 0.06
4379 Evelyn Banks 1 1 15 1 0.06
4380 Frederick Hicks 1 1 15 1 0.06
4381 Gregory Patton 1 1 15 1 0.06
4382 Isabella Smith 1 1 15 1 0.06
4383 Kari Warren 1 1 15 1 0.06
4384 Linus Bonbach 1 1 15 1 0.06
4385 Lisa Harris 1 1 15 1 0.06
4386 Raimund Wernecke 1 1 15 1 0.06
4387 Sabrina Jenkins 1 1 15 1 0.06
4388 Sarah Banks 1 1 15 1 0.06
4389 Sean Harrison 1 1 15 1 0.06
4390 Shelby Contreras 1 1 15 1 0.06
4391 Xiulan Guo 1 1 15 1 0.06
4392 Yang Qin 1 1 15 1 0.06
4393 Alyssa Thompson 1 1 11 2 0.06
4394 Anika Behl 1 1 11 2 0.06
4395 Anneliese Döhn 1 1 11 2 0.06
4396 Benjamin Brown 1 1 11 2 0.06
4397 Benjamin Lee 1 1 11 2 0.06
4398 Bradley Hamilton 1 1 11 2 0.06
4399 Carlos Brown 1 1 11 2 0.06
4400 Chris Tate 1 1 11 2 0.06
4401 Cosima Mülichen 1 1 11 2 0.06
4402 Deborah Wright 1 1 11 2 0.06
4403 Eric Williams 1 1 11 2 0.06
4404 Eva Seidel 1 1 11 2 0.06
4405 Fenna Zwart 1 1 11 2 0.06
4406 Gerhild Patberg 1 1 11 2 0.06
4407 Hilda Plath 1 1 11 2 0.06
4408 Jasmine Lopez 1 1 11 2 0.06
4409 Jennifer Brown 1 1 11 2 0.06
4410 Jeremi Pronobis 1 1 11 2 0.06
4411 Jing Wen 1 1 11 2 0.06
4412 Jo Barnes 1 1 11 2 0.06
4413 Jonathan Williams 1 1 11 2 0.06
4414 Julia Jones 1 1 11 2 0.06
4415 Kari Miranda 1 1 11 2 0.06
4416 Kathleen Sandoval 1 1 11 2 0.06
4417 Keith Clark 1 1 11 2 0.06
4418 Kevin Ruiz 1 1 11 2 0.06
4419 Kristin Barber 1 1 11 2 0.06
4420 Ludger Ladeck 1 1 11 2 0.06
4421 Max Howard 1 1 11 2 0.06
4422 Melissa Dennis 1 1 11 2 0.06
4423 Michael Anderson 1 1 11 2 0.06
4424 Michelle Shepard 1 1 11 2 0.06
4425 Miranda Gonzales 1 1 11 2 0.06
4426 Nakul Agarwal 1 1 11 2 0.06
4427 Nataniel Nazarewicz 1 1 11 2 0.06
4428 Ninetta Spinelli 1 1 11 2 0.06
4429 Paloma Tomaselli 1 1 11 2 0.06
4430 Raymond Wright 1 1 11 2 0.06
4431 Richard Kennedy 1 1 11 2 0.06
4432 Robin Garrett 1 1 11 2 0.06
4433 Ronald Marshall 1 1 11 2 0.06
4434 Róża Bełz 1 1 11 2 0.06
4435 Sabrina Jimenez 1 1 11 2 0.06
4436 Sahil Basak 1 1 11 2 0.06
4437 Stephanie Frank 1 1 11 2 0.06
4438 Sylvia Thompson 1 1 11 2 0.06
4439 Taylor Reed 1 1 11 2 0.06
4440 Valentine Peukert 1 1 11 2 0.06
4441 Valerie Miller 1 1 11 2 0.06
4442 Yang Hao 1 1 11 2 0.06
4443 Benjamin Clark 3 0 3 0 0.06
4444 Maximus Grey 3 0 3 0 0.06
4445 Sophia Brooks 3 0 3 0 0.06
4446 Quinn Rivers 2 1 2 0 0.06
4447 Ryder Lyons 2 1 2 0 0.06
4448 Xiuying Tan 2 1 2 0 0.06
4449 Lunar Echo 1 1 7 3 0.06
4450 Maaya Ishikawa 1 1 7 3 0.06
4451 Paul Dunn 1 1 7 3 0.06
4452 Sayuri Kondo 1 1 7 3 0.06
4453 Torsten Etzold 1 1 7 3 0.06
4454 Veronica Bell 1 1 7 3 0.06
4455 Yui Ito 1 1 7 3 0.06
4456 Amy Powell 1 1 14 1 0.06
4457 Angela Thomas 1 1 14 1 0.06
4458 Angela Villa 1 1 14 1 0.06
4459 Carol Simpson 1 1 14 1 0.06
4460 Cassidy Hubbard 1 1 14 1 0.06
4461 Debra Graham 1 1 14 1 0.06
4462 Donna Hanson 1 1 14 1 0.06
4463 Dylan Schwartz 1 1 14 1 0.06
4464 Edward Evans 1 1 14 1 0.06
4465 Erika Ruiz 1 1 14 1 0.06
4466 Heather Cabrera 1 1 14 1 0.06
4467 James Aguilar 1 1 14 1 0.06
4468 James Henderson 1 1 14 1 0.06
4469 Joel Long 1 1 14 1 0.06
4470 Jörg-Peter Hecker 1 1 14 1 0.06
4471 Katherine Johnston 1 1 14 1 0.06
4472 Kelly Castro 1 1 14 1 0.06
4473 Laura Gordon 1 1 14 1 0.06
4474 Melissa Arias 1 1 14 1 0.06
4475 Michael Nixon 1 1 14 1 0.06
4476 Midnight Prism 1 1 14 1 0.06
4477 Milica Textor-Sorgatz 1 1 14 1 0.06
4478 Nathan Jones 1 1 14 1 0.06
4479 Oliver Henschel 1 1 14 1 0.06
4480 Patrick Cook 1 1 14 1 0.06
4481 Ryan Leblanc 1 1 14 1 0.06
4482 Shawn Johnson 1 1 14 1 0.06
4483 Stanley Hamilton 1 1 14 1 0.06
4484 Stephen Lawrence 1 1 14 1 0.06
4485 Susan Christian 1 1 14 1 0.06
4486 Terri Reynolds 1 1 14 1 0.06
4487 Ayse Häring 1 1 10 2 0.06
4488 Christina Smith 1 1 10 2 0.06
4489 Courtney Bennett 1 1 10 2 0.06
4490 Gisa Hettner-Adler 1 1 10 2 0.06
4491 Gloria Gutknecht 1 1 10 2 0.06
4492 Jie Han 1 1 10 2 0.06
4493 Jose Garrett 1 1 10 2 0.06
4494 Lori Martin 1 1 10 2 0.06
4495 Pierre Ruppert 1 1 10 2 0.06
4496 Qiang Hu 1 1 10 2 0.06
4497 Robert Combs 1 1 10 2 0.06
4498 The Omega Collective 1 1 10 2 0.06
4499 Theodore Shaw 1 1 10 2 0.06
4500 William Tyler 1 1 10 2 0.06
4501 Xia Zou 1 1 10 2 0.06
4502 Antoinette Bertin 1 1 6 3 0.06
4503 Cynthia Hayes 1 1 6 3 0.06
4504 Kimberly Keller 1 1 6 3 0.06
4505 Ping Zhu 1 1 6 3 0.06
4506 Ronnie Rodriguez 1 1 6 3 0.06
4507 Yang Tian 1 1 6 3 0.06
4508 Alfredo del Pujol 1 1 13 1 0.06
4509 Amancio Delgado Morata 1 1 13 1 0.06
4510 Amanda Reyes 1 1 13 1 0.06
4511 Antonio Allen 1 1 13 1 0.06
4512 Ariel Falcó Narváez 1 1 13 1 0.06
4513 Arthur Colas 1 1 13 1 0.06
4514 Bartolomeo Castellitto-Gritti 1 1 13 1 0.06
4515 Benvenuto Castiglione-Pulci 1 1 13 1 0.06
4516 Borja Santamaria-Adán 1 1 13 1 0.06
4517 Corinna Schweitzer 1 1 13 1 0.06
4518 Daniel Martin 1 1 13 1 0.06
4519 Dylan Williams 1 1 13 1 0.06
4520 Elizabeth Adams 1 1 13 1 0.06
4521 Erin Hall 1 1 13 1 0.06
4522 Eveline Gotthard 1 1 13 1 0.06
4523 Fabiana Narváez Vazquez 1 1 13 1 0.06
4524 Gang Shi 1 1 13 1 0.06
4525 Geronimo Lamborghini-Dossetti 1 1 13 1 0.06
4526 Gioele Merisi 1 1 13 1 0.06
4527 Giuliano Anastasi 1 1 13 1 0.06
4528 Hans-Karl Schottin 1 1 13 1 0.06
4529 Hermann Josef Matthäi 1 1 13 1 0.06
4530 Jennifer Curtis 1 1 13 1 0.06
4531 Jessica Phelps 1 1 13 1 0.06
4532 Jessica Smith 1 1 13 1 0.06
4533 Joseph Nichols 1 1 13 1 0.06
4534 Kara Rich 1 1 13 1 0.06
4535 Katherine Olson 1 1 13 1 0.06
4536 Kati Sauer-Rudolph 1 1 13 1 0.06
4537 Kim Washington 1 1 13 1 0.06
4538 Krista Dunn 1 1 13 1 0.06
4539 Luciano Maderna 1 1 13 1 0.06
4540 Mandy Hood 1 1 13 1 0.06
4541 Marc Ward 1 1 13 1 0.06
4542 Maria Morris 1 1 13 1 0.06
4543 Matthew Walker 1 1 13 1 0.06
4544 Mayte Salvà Durán 1 1 13 1 0.06
4545 Melanie Armstrong 1 1 13 1 0.06
4546 Melissa Merritt 1 1 13 1 0.06
4547 Michelle Anderson 1 1 13 1 0.06
4548 Mohamed Cazorla 1 1 13 1 0.06
4549 Naoko Miura 1 1 13 1 0.06
4550 Nico Castellanos García 1 1 13 1 0.06
4551 Paola Condoleo 1 1 13 1 0.06
4552 Paula Mendizábal Alegria 1 1 13 1 0.06
4553 Renata Parpinel 1 1 13 1 0.06
4554 Robert Villanueva 1 1 13 1 0.06
4555 Rodolfo Cutuli 1 1 13 1 0.06
4556 Rosario Pujadas Barceló 1 1 13 1 0.06
4557 Sharon Crawford 1 1 13 1 0.06
4558 Silvio Frías Castejón 1 1 13 1 0.06
4559 Stephanie House 1 1 13 1 0.06
4560 Terri Thomas 1 1 13 1 0.06
4561 Wei Yang 1 1 13 1 0.06
4562 Yang Mao 1 1 13 1 0.06
4563 Alexandra Moreno 1 1 9 2 0.06
4564 Amico Luciani 1 1 9 2 0.06
4565 Anna Vávrová 1 1 9 2 0.06
4566 Ashley Robinson 1 1 9 2 0.06
4567 Caleb Ross 1 1 9 2 0.06
4568 Christine Turner 1 1 9 2 0.06
4569 Courtney Castillo 1 1 9 2 0.06
4570 David Reese 1 1 9 2 0.06
4571 David White 1 1 9 2 0.06
4572 Elmo Calbo 1 1 9 2 0.06
4573 Gary Stephens 1 1 9 2 0.06
4574 Jennifer Davis 1 1 9 2 0.06
4575 Jun Tao 1 1 9 2 0.06
4576 Karsten Löwer 1 1 9 2 0.06
4577 Katie Oliver 1 1 9 2 0.06
4578 Keith Stone 1 1 9 2 0.06
4579 Kelli Jackson 1 1 9 2 0.06
4580 Li Dai 1 1 9 2 0.06
4581 Mackenzie Greene 1 1 9 2 0.06
4582 Mary Combs 1 1 9 2 0.06
4583 Matthieu-Daniel Launay 1 1 9 2 0.06
4584 Megan Bridges 1 1 9 2 0.06
4585 Nancy Mitchell 1 1 9 2 0.06
4586 Nicholas Porter 1 1 9 2 0.06
4587 Nick Kensy 1 1 9 2 0.06
4588 Nicolas Jones 1 1 9 2 0.06
4589 Nicole Bass 1 1 9 2 0.06
4590 Richard Gonzales 1 1 9 2 0.06
4591 Richard Kovář 1 1 9 2 0.06
4592 Ronnie Watkins 1 1 9 2 0.06
4593 Ryan Dunlap 1 1 9 2 0.06
4594 Saira Sood 1 1 9 2 0.06
4595 Sean King 1 1 9 2 0.06
4596 Shelly Lewis 1 1 9 2 0.06
4597 Stefanie Sullivan 1 1 9 2 0.06
4598 Thérèse Guillou 1 1 9 2 0.06
4599 Tristan Chauveau 1 1 9 2 0.06
4600 Warren Fleming 1 1 9 2 0.06
4601 Xiuying Jia 1 1 9 2 0.06
4602 Yves Aubry 1 1 9 2 0.06
4603 Bianca Singleton 1 1 5 3 0.06
4604 Brianna Norris 1 1 5 3 0.06
4605 Elias Rouwenhorst 1 1 5 3 0.06
4606 Erin Smith 1 1 5 3 0.06
4607 Igor Dyś 1 1 5 3 0.06
4608 Jérôme Riou 1 1 5 3 0.06
4609 Michelle Laurent de la Berger 1 1 5 3 0.06
4610 Nicholas Burns 1 1 5 3 0.06
4611 Teresa Bohlander 1 1 5 3 0.06
4612 Aaron Livingston 1 1 12 1 0.05
4613 Aleksandar Kohl 1 1 12 1 0.05
4614 Anay Behl 1 1 12 1 0.05
4615 Angelica Reid 1 1 12 1 0.05
4616 Atenulf Nadi-Vergassola 1 1 12 1 0.05
4617 Brittany Mueller 1 1 12 1 0.05
4618 Cassandra Carroll 1 1 12 1 0.05
4619 Chad Chavez 1 1 12 1 0.05
4620 David Duncan 1 1 12 1 0.05
4621 Emily Sullivan 1 1 12 1 0.05
4622 Eric Morrison 1 1 12 1 0.05
4623 Fabrizia Gualtieri 1 1 12 1 0.05
4624 Folker Jacob-Kreusel 1 1 12 1 0.05
4625 Francis Kaufman 1 1 12 1 0.05
4626 Francisco Kerr 1 1 12 1 0.05
4627 Gail Garrett 1 1 12 1 0.05
4628 Hanako Takahashi 1 1 12 1 0.05
4629 Holly Young 1 1 12 1 0.05
4630 Jie Qin 1 1 12 1 0.05
4631 Johanne Salz 1 1 12 1 0.05
4632 Joseph Preston 1 1 12 1 0.05
4633 Joshua Cobb 1 1 12 1 0.05
4634 Kana Hashimoto 1 1 12 1 0.05
4635 Kimberly Martin 1 1 12 1 0.05
4636 Maria-Luise Flantz 1 1 12 1 0.05
4637 Marissa Holt 1 1 12 1 0.05
4638 Matthew Adams 1 1 12 1 0.05
4639 Michael Hill 1 1 12 1 0.05
4640 Miss Stephanie Middleton 1 1 12 1 0.05
4641 Monique Jefferson 1 1 12 1 0.05
4642 Nidia Menéndez Mateo 1 1 12 1 0.05
4643 Pedro Scholtz 1 1 12 1 0.05
4644 Rachael Stafford 1 1 12 1 0.05
4645 Richard White 1 1 12 1 0.05
4646 Robert Cowan 1 1 12 1 0.05
4647 Sarah Phillips 1 1 12 1 0.05
4648 Sarah Wright 1 1 12 1 0.05
4649 Shamik Jhaveri 1 1 12 1 0.05
4650 Tracey Murphy 1 1 12 1 0.05
4651 Vitus Heinz 1 1 12 1 0.05
4652 William Chan 1 1 12 1 0.05
4653 Zara Dugar 1 1 12 1 0.05
4654 Andrew Peters 1 1 8 2 0.05
4655 Audrey Le Boucher 1 1 8 2 0.05
4656 Benoît Delmas 1 1 8 2 0.05
4657 Donald Cooper 1 1 8 2 0.05
4658 Erika Austin 1 1 8 2 0.05
4659 Henny Caspar 1 1 8 2 0.05
4660 Justus Hornig 1 1 8 2 0.05
4661 Kornelia Pędzik 1 1 8 2 0.05
4662 Manon Diaz 1 1 8 2 0.05
4663 Rika Tanaka 1 1 8 2 0.05
4664 Robert Johnson 1 1 8 2 0.05
4665 Travis Gonzales 1 1 8 2 0.05
4666 Melissa Lucero 1 0 14 5 0.05
4667 Thomas Lee 1 0 14 5 0.05
4668 Vít Svoboda 1 0 14 5 0.05
4669 Daisy Rose 1 0 25 2 0.05
4670 Mark West 1 0 25 2 0.05
4671 Sara Johnson 1 0 25 2 0.05
4672 Stephanie Andersen 1 0 25 2 0.05
4673 Tiffany Simmons 1 0 25 2 0.05
4674 Aaron Young 1 1 15 0 0.05
4675 Benjamin Moore 1 1 15 0 0.05
4676 Connor Lopez 1 1 15 0 0.05
4677 Fang Kang 1 1 15 0 0.05
4678 Haley Joseph 1 1 15 0 0.05
4679 Hannah Roberson 1 1 15 0 0.05
4680 Mike Rose 1 1 15 0 0.05
4681 Ming Yin 1 1 15 0 0.05
4682 Robert Rollins 1 1 15 0 0.05
4683 Robin Noble 1 1 15 0 0.05
4684 Timothy Scott 1 1 15 0 0.05
4685 Alexis Hawkins 1 1 11 1 0.05
4686 Alfio Greggio 1 1 11 1 0.05
4687 Alina Ricciardi 1 1 11 1 0.05
4688 Andrea Martin 1 1 11 1 0.05
4689 Angel Henderson 1 1 11 1 0.05
4690 Ashley Waters 1 1 11 1 0.05
4691 Atenulf Pigafetta 1 1 11 1 0.05
4692 Audrey King 1 1 11 1 0.05
4693 Bobby Guzman 1 1 11 1 0.05
4694 Bruno Silvestri 1 1 11 1 0.05
4695 Calixta Moya Gómez 1 1 11 1 0.05
4696 Christopher Aguilar 1 1 11 1 0.05
4697 Cipriano Navone 1 1 11 1 0.05
4698 Craig Ortega 1 1 11 1 0.05
4699 Danielle Martin 1 1 11 1 0.05
4700 Devin Mccoy 1 1 11 1 0.05
4701 Erin Kim 1 1 11 1 0.05
4702 Erin Moses 1 1 11 1 0.05
4703 Eva Squarcione 1 1 11 1 0.05
4704 Fang Zhao 1 1 11 1 0.05
4705 Gilberto Cainero 1 1 11 1 0.05
4706 Hannah Garrett 1 1 11 1 0.05
4707 Heather Miller 1 1 11 1 0.05
4708 James Torres 1 1 11 1 0.05
4709 Jing Cheng 1 1 11 1 0.05
4710 Jing Fan 1 1 11 1 0.05
4711 Jing Zeng 1 1 11 1 0.05
4712 Jonathan Vasquez 1 1 11 1 0.05
4713 Jose White 1 1 11 1 0.05
4714 Juanito Jimenez Hernández 1 1 11 1 0.05
4715 Kaitlin Davis 1 1 11 1 0.05
4716 Kaori Ito 1 1 11 1 0.05
4717 Katrina Maynard 1 1 11 1 0.05
4718 Kenneth Johnson 1 1 11 1 0.05
4719 Kimberly Miles 1 1 11 1 0.05
4720 Kristine Bullock 1 1 11 1 0.05
4721 Li Yan 1 1 11 1 0.05
4722 Luke Murray 1 1 11 1 0.05
4723 Manfred Schmiedecke 1 1 11 1 0.05
4724 Mariana Galeati 1 1 11 1 0.05
4725 Mary Anthony 1 1 11 1 0.05
4726 Micheal Mcguire 1 1 11 1 0.05
4727 Na Gong 1 1 11 1 0.05
4728 Naoko Sato 1 1 11 1 0.05
4729 Olivia Ortega Bermejo 1 1 11 1 0.05
4730 Qiang Yao 1 1 11 1 0.05
4731 Rachel Curry 1 1 11 1 0.05
4732 Raffaello Tarchetti 1 1 11 1 0.05
4733 Samantha Andrews 1 1 11 1 0.05
4734 Samantha Dawson 1 1 11 1 0.05
4735 Sandy Ray 1 1 11 1 0.05
4736 Susan Young 1 1 11 1 0.05
4737 Virginia Ford 1 1 11 1 0.05
4738 Xiuying Lei 1 1 11 1 0.05
4739 Yoko Hashimoto 1 1 11 1 0.05
4740 Adrien Gonzalez-Duval 1 1 7 2 0.05
4741 Alessandra Omma-Paolini 1 1 7 2 0.05
4742 Alexis Griffith 1 1 7 2 0.05
4743 Amanda Kennedy 1 1 7 2 0.05
4744 Anastazja Brózda 1 1 7 2 0.05
4745 Andrea Hamilton 1 1 7 2 0.05
4746 Andrzej Kamyszek 1 1 7 2 0.05
4747 Angela Fisher 1 1 7 2 0.05
4748 Antoine Blanc 1 1 7 2 0.05
4749 Audrey Picard 1 1 7 2 0.05
4750 Carrie Lee 1 1 7 2 0.05
4751 Christina Cunningham 1 1 7 2 0.05
4752 Ehrenfried Werner 1 1 7 2 0.05
4753 Erik Sharp 1 1 7 2 0.05
4754 Felicia Gilbert 1 1 7 2 0.05
4755 Franciszek Wieja 1 1 7 2 0.05
4756 Frederick Houston 1 1 7 2 0.05
4757 Guy Arnaud 1 1 7 2 0.05
4758 Hannah Newton 1 1 7 2 0.05
4759 Hellmut Graf 1 1 7 2 0.05
4760 Jivika Dar 1 1 7 2 0.05
4761 Kelly Carpenter 1 1 7 2 0.05
4762 Kimberly Howe 1 1 7 2 0.05
4763 Lauren Brown 1 1 7 2 0.05
4764 Lauren Turner 1 1 7 2 0.05
4765 Marcelina Wochnik 1 1 7 2 0.05
4766 Mary Frank 1 1 7 2 0.05
4767 Maryse-Margaux Pinto 1 1 7 2 0.05
4768 Megan Franklin 1 1 7 2 0.05
4769 Michael Jacobson 1 1 7 2 0.05
4770 Nancy Dunlap 1 1 7 2 0.05
4771 Natalie Fields 1 1 7 2 0.05
4772 Nicole Lee 1 1 7 2 0.05
4773 Paul Hughes 1 1 7 2 0.05
4774 Rico Weimer 1 1 7 2 0.05
4775 Rika Shimizu 1 1 7 2 0.05
4776 Robert Velasquez 1 1 7 2 0.05
4777 Sandra Hunter 1 1 7 2 0.05
4778 Shannon White 1 1 7 2 0.05
4779 Stephanie Allen 1 1 7 2 0.05
4780 Theresa Webster 1 1 7 2 0.05
4781 Todd Lopez 1 1 7 2 0.05
4782 Whitney Yates 1 1 7 2 0.05
4783 Andrew Coleman 1 1 14 0 0.05
4784 Austin Robinson 1 1 14 0 0.05
4785 Carlo Altera 1 1 14 0 0.05
4786 Dariusz Trupp 1 1 14 0 0.05
4787 Emilio Missoni 1 1 14 0 0.05
4788 Federigo Satta 1 1 14 0 0.05
4789 Felicia Walton 1 1 14 0 0.05
4790 Giovanna Tirabassi 1 1 14 0 0.05
4791 Hugo Visconti 1 1 14 0 0.05
4792 Jaroslav Putz 1 1 14 0 0.05
4793 Jens-Uwe Pruschke 1 1 14 0 0.05
4794 Joe Humphrey 1 1 14 0 0.05
4795 Kathleen Sutton 1 1 14 0 0.05
4796 Kayla Gonzales 1 1 14 0 0.05
4797 Lorenzo Baresi 1 1 14 0 0.05
4798 Ludmila Wesack 1 1 14 0 0.05
4799 Maria Zola 1 1 14 0 0.05
4800 Nicholas Swanson 1 1 14 0 0.05
4801 Olivia Steinberg 1 1 14 0 0.05
4802 Patrick Howard 1 1 14 0 0.05
4803 Susan Scott 1 1 14 0 0.05
4804 Terri Chapman 1 1 14 0 0.05
4805 Vanessa Villadicani 1 1 14 0 0.05
4806 Alejandro Barnes 1 1 10 1 0.05
4807 Alexa Brown 1 1 10 1 0.05
4808 Alexander Budig 1 1 10 1 0.05
4809 Aniela Chojna 1 1 10 1 0.05
4810 Ashley Bennett 1 1 10 1 0.05
4811 Betty Faulkner 1 1 10 1 0.05
4812 Brian Anderson 1 1 10 1 0.05
4813 Charles Paul 1 1 10 1 0.05
4814 Chase Andrews 1 1 10 1 0.05
4815 Christopher Wilcox 1 1 10 1 0.05
4816 Christy Lewis 1 1 10 1 0.05
4817 Daniel Cross 1 1 10 1 0.05
4818 Daniel Miller 1 1 10 1 0.05
4819 David Glenn 1 1 10 1 0.05
4820 Delia Hesse-Wulf 1 1 10 1 0.05
4821 Edwin Diaz 1 1 10 1 0.05
4822 Eric Wallace 1 1 10 1 0.05
4823 Gang Ding 1 1 10 1 0.05
4824 Guenter Stumpf 1 1 10 1 0.05
4825 Herr Rudolph Schaaf 1 1 10 1 0.05
4826 Jacob Grimes 1 1 10 1 0.05
4827 James Cruz 1 1 10 1 0.05
4828 James Vaughn 1 1 10 1 0.05
4829 James White 1 1 10 1 0.05
4830 Janet Cunningham 1 1 10 1 0.05
4831 Jasmine Hammond 1 1 10 1 0.05
4832 Johnny Smith 1 1 10 1 0.05
4833 Jorge Brown 1 1 10 1 0.05
4834 Justin Allen 1 1 10 1 0.05
4835 Karen Schultz 1 1 10 1 0.05
4836 Kevin Simmons 1 1 10 1 0.05
4837 Kimberly Knight 1 1 10 1 0.05
4838 Kirk Griffin 1 1 10 1 0.05
4839 Kyle Myers 1 1 10 1 0.05
4840 Linda Chase 1 1 10 1 0.05
4841 Lonnie Estrada 1 1 10 1 0.05
4842 Louis Brown 1 1 10 1 0.05
4843 Mark Stokes 1 1 10 1 0.05
4844 Mehmet Junk 1 1 10 1 0.05
4845 Melody Cuevas 1 1 10 1 0.05
4846 Michael Frank 1 1 10 1 0.05
4847 Michael Pearson 1 1 10 1 0.05
4848 Michael Williamson 1 1 10 1 0.05
4849 Nicholas Moss 1 1 10 1 0.05
4850 Rachel Rios 1 1 10 1 0.05
4851 Rainer Mans 1 1 10 1 0.05
4852 Ryan Baxter 1 1 10 1 0.05
4853 Sami Bohnbach 1 1 10 1 0.05
4854 Sarah Benson 1 1 10 1 0.05
4855 Sonia Porzio 1 1 10 1 0.05
4856 Stanley Roberts 1 1 10 1 0.05
4857 Tammie Johnson 1 1 10 1 0.05
4858 Tao Tan 1 1 10 1 0.05
4859 Temistocle Vivaldi-Amato 1 1 10 1 0.05
4860 Thomas Leon 1 1 10 1 0.05
4861 Todd Mcbride 1 1 10 1 0.05
4862 Wei Song 1 1 10 1 0.05
4863 Whisper Cascade 1 1 10 1 0.05
4864 William Vollbrecht 1 1 10 1 0.05
4865 Wiltrud Hübel 1 1 10 1 0.05
4866 Yan Mao 1 1 10 1 0.05
4867 Yan Pan 1 1 10 1 0.05
4868 Yang Shen 1 1 10 1 0.05
4869 Yvonne Powell 1 1 10 1 0.05
4870 Amanda Frazier 1 1 6 2 0.05
4871 Anthony Farmer 1 1 6 2 0.05
4872 Austin Martin 1 1 6 2 0.05
4873 Beverly Powell 1 1 6 2 0.05
4874 Cody Rowe 1 1 6 2 0.05
4875 Debra Anderson 1 1 6 2 0.05
4876 Grzegorz Rorat 1 1 6 2 0.05
4877 James Mcmahon 1 1 6 2 0.05
4878 Jeffrey Hill 1 1 6 2 0.05
4879 John Sanchez 1 1 6 2 0.05
4880 Kenichi Matsuda 1 1 6 2 0.05
4881 Maite Cuevas Caballero 1 1 6 2 0.05
4882 Milena Anichini-Barracco 1 1 6 2 0.05
4883 Nicolas Marsh 1 1 6 2 0.05
4884 Samantha Hughes 1 1 6 2 0.05
4885 Shannon Oneill 1 1 6 2 0.05
4886 Steven Medina 1 1 6 2 0.05
4887 Tao Wu 1 1 6 2 0.05
4888 Tomoya Shimizu 1 1 6 2 0.05
4889 Travis Tucker 1 1 6 2 0.05
4890 Tomoya Kato 1 1 2 3 0.05
4891 Emily Perkins 1 1 13 0 0.05
4892 John Johnston 1 1 13 0 0.05
4893 Joseph Rodriguez 1 1 13 0 0.05
4894 Kimberly Lee 1 1 13 0 0.05
4895 Kristina Serrano 1 1 13 0 0.05
4896 Melissa Johnson 1 1 13 0 0.05
4897 Amy Marshall 1 1 9 1 0.05
4898 Andrea Brooks 1 1 9 1 0.05
4899 Andrew Williamson 1 1 9 1 0.05
4900 Anka Spieß 1 1 9 1 0.05
4901 Anthony Garcia 1 1 9 1 0.05
4902 Ashley Jennings 1 1 9 1 0.05
4903 Bianka Herrmann 1 1 9 1 0.05
4904 Brent Larsen 1 1 9 1 0.05
4905 Brian Morales 1 1 9 1 0.05
4906 Brooke Ortiz 1 1 9 1 0.05
4907 Carlos Mccann 1 1 9 1 0.05
4908 Casey Price 1 1 9 1 0.05
4909 Catherine Price 1 1 9 1 0.05
4910 Chelsea Paul 1 1 9 1 0.05
4911 Cheryl Brown 1 1 9 1 0.05
4912 Chiyo Suzuki 1 1 9 1 0.05
4913 Christine Jennings 1 1 9 1 0.05
4914 Christopher Cox 1 1 9 1 0.05
4915 Christopher Miller 1 1 9 1 0.05
4916 Christopher Wilson 1 1 9 1 0.05
4917 Cody Lopez 1 1 9 1 0.05
4918 Courtney Smith 1 1 9 1 0.05
4919 Crystal Yu 1 1 9 1 0.05
4920 Danielle Franklin 1 1 9 1 0.05
4921 Dennis Fields 1 1 9 1 0.05
4922 Donald Oliver 1 1 9 1 0.05
4923 Edward Matthews 1 1 9 1 0.05
4924 Elena Nibali 1 1 9 1 0.05
4925 Elenore Wulff 1 1 9 1 0.05
4926 Erica Hill 1 1 9 1 0.05
4927 Erika Martin 1 1 9 1 0.05
4928 Fabian Durys 1 1 9 1 0.05
4929 Fabienne Klemm-Kade 1 1 9 1 0.05
4930 Fang Fan 1 1 9 1 0.05
4931 Fang Zhang 1 1 9 1 0.05
4932 Frank Osborne 1 1 9 1 0.05
4933 Gioffre Garobbio 1 1 9 1 0.05
4934 Holly Horton 1 1 9 1 0.05
4935 Hubertine Plath 1 1 9 1 0.05
4936 Ida Burzec 1 1 9 1 0.05
4937 Irmtraut Benthin 1 1 9 1 0.05
4938 Jaclyn Smith 1 1 9 1 0.05
4939 James Bishop 1 1 9 1 0.05
4940 Janice Poole 1 1 9 1 0.05
4941 Jing Liu 1 1 9 1 0.05
4942 Jose Brown 1 1 9 1 0.05
4943 Joshua Shelton 1 1 9 1 0.05
4944 Juergen Carsten 1 1 9 1 0.05
4945 Justin Hensley 1 1 9 1 0.05
4946 Justin Powers 1 1 9 1 0.05
4947 Kana Mori 1 1 9 1 0.05
4948 Kaori Watanabe 1 1 9 1 0.05
4949 Karen Robinson 1 1 9 1 0.05
4950 Kathryn Smith 1 1 9 1 0.05
4951 Kenneth Evans 1 1 9 1 0.05
4952 Kristen Hamilton 1 1 9 1 0.05
4953 Kristen Porter 1 1 9 1 0.05
4954 Laura Harrison 1 1 9 1 0.05
4955 Lauren Mclean 1 1 9 1 0.05
4956 Leah Dixon 1 1 9 1 0.05
4957 Li Qin 1 1 9 1 0.05
4958 Magdalene Fliegner 1 1 9 1 0.05
4959 Manuel Wu 1 1 9 1 0.05
4960 Mark Heath 1 1 9 1 0.05
4961 Matthew Chapman 1 1 9 1 0.05
4962 Maurice Nguyen 1 1 9 1 0.05
4963 Melissa Avila 1 1 9 1 0.05
4964 Ming Dong 1 1 9 1 0.05
4965 Ming Yi 1 1 9 1 0.05
4966 Mitchell Larson 1 1 9 1 0.05
4967 Nadia Mazzanti 1 1 9 1 0.05
4968 Nicholas Bond 1 1 9 1 0.05
4969 Nicholas Lawrence 1 1 9 1 0.05
4970 Olivia Clark 1 1 9 1 0.05
4971 Peter Banks 1 1 9 1 0.05
4972 Rebecca Wheeler 1 1 9 1 0.05
4973 Renata Manolesso 1 1 9 1 0.05
4974 Robert Gonzalez 1 1 9 1 0.05
4975 Rudi Bolander-Zimmer 1 1 9 1 0.05
4976 Ryan Johnston 1 1 9 1 0.05
4977 Scott Kennedy 1 1 9 1 0.05
4978 Sophia Zaguri 1 1 9 1 0.05
4979 Stacey Bruce 1 1 9 1 0.05
4980 Stephen Thompson 1 1 9 1 0.05
4981 Summer Friedman 1 1 9 1 0.05
4982 Susan Stewart 1 1 9 1 0.05
4983 Sylvia Lawrence 1 1 9 1 0.05
4984 Taichi Sasaki 1 1 9 1 0.05
4985 Terry Harris 1 1 9 1 0.05
4986 Thomas Lopez 1 1 9 1 0.05
4987 Tim Johnson 1 1 9 1 0.05
4988 Tola Cymer 1 1 9 1 0.05
4989 Tomoya Nakamura 1 1 9 1 0.05
4990 Tomoya Takahashi 1 1 9 1 0.05
4991 Urszula Henke 1 1 9 1 0.05
4992 Wei Chen 1 1 9 1 0.05
4993 Wojciech Jarmuła 1 1 9 1 0.05
4994 Xiulan Bai 1 1 9 1 0.05
4995 Yan Dong 1 1 9 1 0.05
4996 Yong Cheng 1 1 9 1 0.05
4997 Yong Kang 1 1 9 1 0.05
4998 Yong Luo 1 1 9 1 0.05
4999 Yuki Hashimoto 1 1 9 1 0.05
5000 Olga Kalinka 1 0 15 4 0.05
5001 Adira Ramaswamy 1 1 5 2 0.05
5002 Catherine Bolton 1 1 5 2 0.05
5003 Christina Villa 1 1 5 2 0.05
5004 Christine Hauffer 1 1 5 2 0.05
5005 Daniela Scholtz 1 1 5 2 0.05
5006 Drishya Setty 1 1 5 2 0.05
5007 Jennifer Alvarez 1 1 5 2 0.05
5008 Jonathan Adams 1 1 5 2 0.05
5009 Kristen Edwards 1 1 5 2 0.05
5010 Laurent-Léon Morvan 1 1 5 2 0.05
5011 Lori Massey 1 1 5 2 0.05
5012 Lucie Tanguy 1 1 5 2 0.05
5013 Patrick Banks 1 1 5 2 0.05
5014 Philippe Carre de la Humbert 1 1 5 2 0.05
5015 Randy Stewart 1 1 5 2 0.05
5016 Ronald Ramirez 1 1 5 2 0.05
5017 Samar Dutta 1 1 5 2 0.05
5018 Sarah Patrick 1 1 5 2 0.05
5019 Subterranean Whispers 1 1 5 2 0.05
5020 William White 1 1 5 2 0.05
5021 Adam Smith 1 1 12 0 0.05
5022 Adrian Ross 1 1 12 0 0.05
5023 Alexander Rose 1 1 12 0 0.05
5024 Audrey Fritz 1 1 12 0 0.05
5025 Bartek Heleniak 1 1 12 0 0.05
5026 Christopher Fischer 1 1 12 0 0.05
5027 Craig Villanueva 1 1 12 0 0.05
5028 David Robinson 1 1 12 0 0.05
5029 Denny Scholl-Vogt 1 1 12 0 0.05
5030 Dominique Newton 1 1 12 0 0.05
5031 Dylan Miller 1 1 12 0 0.05
5032 Elaine Miller 1 1 12 0 0.05
5033 Felizitas Hermann 1 1 12 0 0.05
5034 Jerzy Majczyna 1 1 12 0 0.05
5035 Jonathan Gonzales 1 1 12 0 0.05
5036 Karen Rodriguez 1 1 12 0 0.05
5037 Mackenzie Garza 1 1 12 0 0.05
5038 Magret Staude 1 1 12 0 0.05
5039 Patricia Maynard 1 1 12 0 0.05
5040 Paul Hale 1 1 12 0 0.05
5041 Rebecca Bennett 1 1 12 0 0.05
5042 Regina Yang 1 1 12 0 0.05
5043 Rhonda Mendez 1 1 12 0 0.05
5044 Sara Chung 1 1 12 0 0.05
5045 Sonia Trochimiuk 1 1 12 0 0.05
5046 Summer Diaz 1 1 12 0 0.05
5047 Thomas Robinson 1 1 12 0 0.05
5048 Xiulan Qian 1 1 12 0 0.05
5049 Alexander Ramirez 1 1 8 1 0.05
5050 Amanda Boitani 1 1 8 1 0.05
5051 Amanda Lee 1 1 8 1 0.05
5052 Amy Frazier 1 1 8 1 0.05
5053 Anastazja Siedlik 1 1 8 1 0.05
5054 Andrew Gordon 1 1 8 1 0.05
5055 Anna Coleman 1 1 8 1 0.05
5056 Annetta Dallapé 1 1 8 1 0.05
5057 Anthony Navarro 1 1 8 1 0.05
5058 Baptist Adolph 1 1 8 1 0.05
5059 Beverly Lloyd 1 1 8 1 0.05
5060 Bobby Ruiz 1 1 8 1 0.05
5061 Bradley Mendez 1 1 8 1 0.05
5062 Breanna Neal 1 1 8 1 0.05
5063 Calcedonio Abbagnale 1 1 8 1 0.05
5064 Carrie Lang 1 1 8 1 0.05
5065 Carrie Pace 1 1 8 1 0.05
5066 Cesar Mcpherson 1 1 8 1 0.05
5067 Chao Feng 1 1 8 1 0.05
5068 Crystal Caldwell 1 1 8 1 0.05
5069 Dana Griffin 1 1 8 1 0.05
5070 Darlene Chase 1 1 8 1 0.05
5071 David Higgins 1 1 8 1 0.05
5072 Dean Mullins 1 1 8 1 0.05
5073 Deborah Odonnell 1 1 8 1 0.05
5074 Diana Johnson 1 1 8 1 0.05
5075 Eduardo Gonzalez 1 1 8 1 0.05
5076 Elizabeth Evans 1 1 8 1 0.05
5077 Elizabeth Martin 1 1 8 1 0.05
5078 Federico Offredi 1 1 8 1 0.05
5079 Guido Sbarbaro 1 1 8 1 0.05
5080 Guiying Xiong 1 1 8 1 0.05
5081 Haley Williams 1 1 8 1 0.05
5082 Heer Balan 1 1 8 1 0.05
5083 Heinz-Günter Kallert 1 1 8 1 0.05
5084 Ishaan Mahal 1 1 8 1 0.05
5085 James Gibson 1 1 8 1 0.05
5086 Jamie Davis 1 1 8 1 0.05
5087 Jie Kong 1 1 8 1 0.05
5088 Joel Collins 1 1 8 1 0.05
5089 Joseph Brooks 1 1 8 1 0.05
5090 Joyce Jackson 1 1 8 1 0.05
5091 Jun Ma 1 1 8 1 0.05
5092 Justin Morse 1 1 8 1 0.05
5093 Kajetan Ryszkiewicz 1 1 8 1 0.05
5094 Karen Garcia 1 1 8 1 0.05
5095 Katherine Watts 1 1 8 1 0.05
5096 Katrina Valentine 1 1 8 1 0.05
5097 Kristen Rice 1 1 8 1 0.05
5098 Kyle Daniel 1 1 8 1 0.05
5099 Laura Roberts 1 1 8 1 0.05
5100 Laura Sloan 1 1 8 1 0.05
5101 Leah Robinson 1 1 8 1 0.05
5102 Lei Xia 1 1 8 1 0.05
5103 Liwia Głuszko 1 1 8 1 0.05
5104 Lorenzo Orengo 1 1 8 1 0.05
5105 Madison Lopez 1 1 8 1 0.05
5106 Maria Fletcher 1 1 8 1 0.05
5107 Maria-Luise Klemm 1 1 8 1 0.05
5108 Mary Gomez 1 1 8 1 0.05
5109 Matthew Caldwell 1 1 8 1 0.05
5110 Matthew Gibson 1 1 8 1 0.05
5111 Matthew Li 1 1 8 1 0.05
5112 Matthew Roberson 1 1 8 1 0.05
5113 Michael Ramirez 1 1 8 1 0.05
5114 Mieszko Dorna 1 1 8 1 0.05
5115 Nadja Gnatz 1 1 8 1 0.05
5116 Nino Carfagna-Totino 1 1 8 1 0.05
5117 Oskar Radzewicz 1 1 8 1 0.05
5118 Patrick Davenport 1 1 8 1 0.05
5119 Paul Burns 1 1 8 1 0.05
5120 Robert Lopez 1 1 8 1 0.05
5121 Roger Allen 1 1 8 1 0.05
5122 Ruy Villa Coloma 1 1 8 1 0.05
5123 Ryan Bailey 1 1 8 1 0.05
5124 Samantha Walters 1 1 8 1 0.05
5125 Sara Hołubowicz 1 1 8 1 0.05
5126 Sean Aguirre 1 1 8 1 0.05
5127 Tammy Washington 1 1 8 1 0.05
5128 Tao Xiao 1 1 8 1 0.05
5129 Terry Castillo 1 1 8 1 0.05
5130 Thomas Stephens 1 1 8 1 0.05
5131 Tiffany Mckee 1 1 8 1 0.05
5132 Todd Reed 1 1 8 1 0.05
5133 Valerie Stafford 1 1 8 1 0.05
5134 Wendy Flores 1 1 8 1 0.05
5135 Wiktor Kazimierczyk 1 1 8 1 0.05
5136 William Hawkins 1 1 8 1 0.05
5137 William Lynch 1 1 8 1 0.05
5138 Xia He 1 1 8 1 0.05
5139 Yong Li 1 1 8 1 0.05
5140 Yuvaan Chopra 1 1 8 1 0.05
5141 Amy Ochoa 1 1 4 2 0.05
5142 Calvin Kemp 1 1 4 2 0.05
5143 Erica Padilla 1 1 4 2 0.05
5144 Jason Lopez 1 1 4 2 0.05
5145 Juanita Galván 1 1 4 2 0.05
5146 Julia Smith 1 1 4 2 0.05
5147 Khushi Upadhyay 1 1 4 2 0.05
5148 Kumiko Fujita 1 1 4 2 0.05
5149 Maaya Tanaka 1 1 4 2 0.05
5150 Marcel Kutek 1 1 4 2 0.05
5151 Matthew Garcia 1 1 4 2 0.05
5152 Patrick Carney 1 1 4 2 0.05
5153 Paula Palmer 1 1 4 2 0.05
5154 William Rojas 1 1 4 2 0.05
5155 Yuta Sato 1 1 4 2 0.05
5156 Alex Mathis 1 1 11 0 0.05
5157 Allen Smith 1 1 11 0 0.05
5158 Amy Ramos 1 1 11 0 0.05
5159 Amy Wood 1 1 11 0 0.05
5160 Brenda Day 1 1 11 0 0.05
5161 Chelsea Young 1 1 11 0 0.05
5162 Christopher Nelson 1 1 11 0 0.05
5163 Dana Patterson 1 1 11 0 0.05
5164 Edward Dixon 1 1 11 0 0.05
5165 Edward Hamilton 1 1 11 0 0.05
5166 Ellen Bradley 1 1 11 0 0.05
5167 Garrett Dixon 1 1 11 0 0.05
5168 Jasmine Nichols 1 1 11 0 0.05
5169 Jason Stevens 1 1 11 0 0.05
5170 Jessica Brown 1 1 11 0 0.05
5171 Jose Morse 1 1 11 0 0.05
5172 Joshua Mckenzie 1 1 11 0 0.05
5173 Juan Tian 1 1 11 0 0.05
5174 Kelly Kim 1 1 11 0 0.05
5175 Kristen Carlson 1 1 11 0 0.05
5176 Leah Thomas 1 1 11 0 0.05
5177 Lisa Wright 1 1 11 0 0.05
5178 Luis Green 1 1 11 0 0.05
5179 Megan Diaz 1 1 11 0 0.05
5180 Nicole Hodges 1 1 11 0 0.05
5181 Ryan Carter 1 1 11 0 0.05
5182 Stephen Davis 1 1 11 0 0.05
5183 Timothy Watson 1 1 11 0 0.05
5184 Willie Johnson 1 1 11 0 0.05
5185 Yong Fang 1 1 11 0 0.05
5186 Alexander Rivera 1 1 7 1 0.05
5187 Alfredo Roth 1 1 7 1 0.05
5188 Allison Duran 1 1 7 1 0.05
5189 Amanda Ramos 1 1 7 1 0.05
5190 Amy Coleman 1 1 7 1 0.05
5191 Amy Gutierrez 1 1 7 1 0.05
5192 Andrea Obrien 1 1 7 1 0.05
5193 Ann Petersen 1 1 7 1 0.05
5194 Antonia Farinelli 1 1 7 1 0.05
5195 Antonia Mülichen 1 1 7 1 0.05
5196 Brandon Mccormick 1 1 7 1 0.05
5197 Brandon Payne 1 1 7 1 0.05
5198 Brian Perez 1 1 7 1 0.05
5199 Brian Ramos 1 1 7 1 0.05
5200 Bryan Sandoval 1 1 7 1 0.05
5201 Carl Rush 1 1 7 1 0.05
5202 Chao Huang 1 1 7 1 0.05
5203 Christian Rojas 1 1 7 1 0.05
5204 Ciro Campano 1 1 7 1 0.05
5205 Claudius Geißler 1 1 7 1 0.05
5206 Cynthia Robbins 1 1 7 1 0.05
5207 Dana Martin 1 1 7 1 0.05
5208 Daniel Aubry 1 1 7 1 0.05
5209 Daryl Parsons 1 1 7 1 0.05
5210 David Bowen 1 1 7 1 0.05
5211 Deborah Davis 1 1 7 1 0.05
5212 Debra Lam 1 1 7 1 0.05
5213 Derek Carpenter 1 1 7 1 0.05
5214 Dionigi Granatelli 1 1 7 1 0.05
5215 Donna Bennett 1 1 7 1 0.05
5216 Egon Wilmsen-Röhrdanz 1 1 7 1 0.05
5217 Elena Germano 1 1 7 1 0.05
5218 Elisa Zoppetti-Venturi 1 1 7 1 0.05
5219 Elizabeth Newton PhD 1 1 7 1 0.05
5220 Eugene Turner 1 1 7 1 0.05
5221 Florence Hermighausen 1 1 7 1 0.05
5222 Gabriel Seip 1 1 7 1 0.05
5223 Gang Gong 1 1 7 1 0.05
5224 Gang Song 1 1 7 1 0.05
5225 Greco Guarato 1 1 7 1 0.05
5226 Guido Conti 1 1 7 1 0.05
5227 Guiying Yu 1 1 7 1 0.05
5228 Hannes Ziegert 1 1 7 1 0.05
5229 Heidi Bradley 1 1 7 1 0.05
5230 Heidi Richardson 1 1 7 1 0.05
5231 Holly Riley 1 1 7 1 0.05
5232 Ignacy Henke 1 1 7 1 0.05
5233 Jacqueline Lawson 1 1 7 1 0.05
5234 James Dudley 1 1 7 1 0.05
5235 James Valentine 1 1 7 1 0.05
5236 Jamie Rodgers 1 1 7 1 0.05
5237 Jason Garcia 1 1 7 1 0.05
5238 Javi Fuente Benavente 1 1 7 1 0.05
5239 Jenny Dunn 1 1 7 1 0.05
5240 Jeremy Rodriguez 1 1 7 1 0.05
5241 Jerzy Ksel 1 1 7 1 0.05
5242 Jessica Bradley 1 1 7 1 0.05
5243 Jie Chang 1 1 7 1 0.05
5244 Jie Shi 1 1 7 1 0.05
5245 Jill Conley 1 1 7 1 0.05
5246 Joseph Alvarez 1 1 7 1 0.05
5247 Julie Mcgrath 1 1 7 1 0.05
5248 Julie Rowe 1 1 7 1 0.05
5249 Kari Dillon 1 1 7 1 0.05
5250 Kathy Phillips 1 1 7 1 0.05
5251 Kelsey Martinez 1 1 7 1 0.05
5252 Kenneth Moore 1 1 7 1 0.05
5253 Kent Chen 1 1 7 1 0.05
5254 Konstanty Drobek 1 1 7 1 0.05
5255 Li Xiang 1 1 7 1 0.05
5256 Lilla Giammusso 1 1 7 1 0.05
5257 Marcin Bernatek 1 1 7 1 0.05
5258 Marina Boucsein 1 1 7 1 0.05
5259 Marissa Evans 1 1 7 1 0.05
5260 Mariusz Burzec 1 1 7 1 0.05
5261 Mark Stein 1 1 7 1 0.05
5262 Matthew Crawford 1 1 7 1 0.05
5263 Matthew Williams 1 1 7 1 0.05
5264 Mattias Pohl 1 1 7 1 0.05
5265 Meagan Hendrix 1 1 7 1 0.05
5266 Megan Garcia 1 1 7 1 0.05
5267 Michael Buchanan 1 1 7 1 0.05
5268 Michael Frederick 1 1 7 1 0.05
5269 Mituru Matsumoto 1 1 7 1 0.05
5270 Na Lin 1 1 7 1 0.05
5271 Naoki Sato 1 1 7 1 0.05
5272 Nichole Moore 1 1 7 1 0.05
5273 Patricia Massey 1 1 7 1 0.05
5274 Paul Baum 1 1 7 1 0.05
5275 Phillip Martinez 1 1 7 1 0.05
5276 Ping Qiu 1 1 7 1 0.05
5277 Regina Davis 1 1 7 1 0.05
5278 Rika Maeda 1 1 7 1 0.05
5279 Robert Stewart 1 1 7 1 0.05
5280 Russell Bates 1 1 7 1 0.05
5281 Salvi Giorgetti 1 1 7 1 0.05
5282 Sarah Craig 1 1 7 1 0.05
5283 Sarah Harrison 1 1 7 1 0.05
5284 Satomi Inoue 1 1 7 1 0.05
5285 Sole Gagliano-Conte 1 1 7 1 0.05
5286 Stephanie Grant 1 1 7 1 0.05
5287 Suzanne Davis 1 1 7 1 0.05
5288 Tammy Griffin 1 1 7 1 0.05
5289 Taylor Pace 1 1 7 1 0.05
5290 Teresa Rivera 1 1 7 1 0.05
5291 Terrance Velazquez 1 1 7 1 0.05
5292 Theda Rosemann 1 1 7 1 0.05
5293 Thomas Miranda 1 1 7 1 0.05
5294 Théodore de la Chauvet 1 1 7 1 0.05
5295 Traci Thomas 1 1 7 1 0.05
5296 Wanda Smith 1 1 7 1 0.05
5297 Whitney Humphrey 1 1 7 1 0.05
5298 Whitney Torres 1 1 7 1 0.05
5299 William Porter 1 1 7 1 0.05
5300 Xia Peng 1 1 7 1 0.05
5301 Yan Yao 1 1 7 1 0.05
5302 Li Fang 1 0 13 4 0.05
5303 Antonia Malaparte-Leonardi 1 1 3 2 0.05
5304 Elizabeth Myers 1 1 3 2 0.05
5305 Erica Smith 1 1 3 2 0.05
5306 Nikol Hrubá 1 1 3 2 0.05
5307 Patrick Leleu 1 1 3 2 0.05
5308 Rosita Vollbrecht 1 1 3 2 0.05
5309 Rémy-Richard Devaux 1 1 3 2 0.05
5310 Brian Brewer 1 1 10 0 0.05
5311 Brittany Holmes 1 1 10 0 0.05
5312 Casey Farrell 1 1 10 0 0.05
5313 Isabell Wulff 1 1 10 0 0.05
5314 Jackson Monroe 1 1 10 0 0.05
5315 Loïs Knoers 1 1 10 0 0.05
5316 Megan Raymond 1 1 10 0 0.05
5317 Michael Bradford 1 1 10 0 0.05
5318 Randall Johnson 1 1 10 0 0.05
5319 Tao Qiu 1 1 10 0 0.05
5320 Abigaíl Tovar Sainz 1 1 6 1 0.04
5321 Adrian Miller 1 1 6 1 0.04
5322 Alexej Klotz 1 1 6 1 0.04
5323 Alfonso Albero Cervera 1 1 6 1 0.04
5324 Alfred Tschentscher 1 1 6 1 0.04
5325 Anastasio Jiménez Ferrer 1 1 6 1 0.04
5326 Annegret Nerger 1 1 6 1 0.04
5327 Ashley Brady 1 1 6 1 0.04
5328 Autumn Walker 1 1 6 1 0.04
5329 Barbara Fox 1 1 6 1 0.04
5330 Barry Wilkerson 1 1 6 1 0.04
5331 Bartek Tatarczyk 1 1 6 1 0.04
5332 Benoît Blanc 1 1 6 1 0.04
5333 Bianca Rice 1 1 6 1 0.04
5334 Brandon Barton 1 1 6 1 0.04
5335 Brett Larson 1 1 6 1 0.04
5336 Brian Brown 1 1 6 1 0.04
5337 Brian Gross 1 1 6 1 0.04
5338 Cassandra Washington 1 1 6 1 0.04
5339 Cayetana Bernal-Ocaña 1 1 6 1 0.04
5340 Charo Cañas Saez 1 1 6 1 0.04
5341 Cheryl Gibson 1 1 6 1 0.04
5342 Cheryl Walker 1 1 6 1 0.04
5343 Christopher Gutierrez 1 1 6 1 0.04
5344 Christopher Owens 1 1 6 1 0.04
5345 Christopher Whitaker 1 1 6 1 0.04
5346 Concepción Ripoll 1 1 6 1 0.04
5347 Cynthia Johns 1 1 6 1 0.04
5348 Damini Datta 1 1 6 1 0.04
5349 Debbie Fuller 1 1 6 1 0.04
5350 Dishani Goswami 1 1 6 1 0.04
5351 Divit Khanna 1 1 6 1 0.04
5352 Ehsaan Lall 1 1 6 1 0.04
5353 Elfriede Reuter 1 1 6 1 0.04
5354 Elsbeth Löwer 1 1 6 1 0.04
5355 Emelina Verdejo Otero 1 1 6 1 0.04
5356 Eric Shaw 1 1 6 1 0.04
5357 Evelína Kučerová 1 1 6 1 0.04
5358 Gang Meng 1 1 6 1 0.04
5359 Gerhart Wieloch 1 1 6 1 0.04
5360 Glenn Lewis 1 1 6 1 0.04
5361 Gloria Lewis 1 1 6 1 0.04
5362 Gregory Jones 1 1 6 1 0.04
5363 Hansh Hayer 1 1 6 1 0.04
5364 Herr Stephan Tschentscher 1 1 6 1 0.04
5365 Holly Stephens 1 1 6 1 0.04
5366 Ian Dorsey 1 1 6 1 0.04
5367 Inés del Valenciano 1 1 6 1 0.04
5368 Ivana Din 1 1 6 1 0.04
5369 Jacob Mcmahon 1 1 6 1 0.04
5370 Jan Hruška 1 1 6 1 0.04
5371 Jan Zadworny 1 1 6 1 0.04
5372 Jennifer Cox 1 1 6 1 0.04
5373 Jennifer Flores 1 1 6 1 0.04
5374 Jenny Rodriguez 1 1 6 1 0.04
5375 Jerry Ruiz 1 1 6 1 0.04
5376 Jessica Lucas 1 1 6 1 0.04
5377 Jessica Wright 1 1 6 1 0.04
5378 Jill Reynolds 1 1 6 1 0.04
5379 Jing Chen 1 1 6 1 0.04
5380 Jing Shao 1 1 6 1 0.04
5381 Jocelyn Davis 1 1 6 1 0.04
5382 John Brewer 1 1 6 1 0.04
5383 John Russo 1 1 6 1 0.04
5384 Johnathan Clark 1 1 6 1 0.04
5385 Joseph Ferguson 1 1 6 1 0.04
5386 Julita Łuszczak 1 1 6 1 0.04
5387 Karol Indyk 1 1 6 1 0.04
5388 Katherine Preston 1 1 6 1 0.04
5389 Katie Anderson 1 1 6 1 0.04
5390 Kelly Gilbert 1 1 6 1 0.04
5391 Kelly Reyes 1 1 6 1 0.04
5392 Kelly Williams 1 1 6 1 0.04
5393 Kim Preston 1 1 6 1 0.04
5394 Kimberly Cain 1 1 6 1 0.04
5395 Kornel Troka 1 1 6 1 0.04
5396 Lagan Bhalla 1 1 6 1 0.04
5397 Leif Bien 1 1 6 1 0.04
5398 Leslie Flynn 1 1 6 1 0.04
5399 Lisa Brooks 1 1 6 1 0.04
5400 Lorenz Förster-Barth 1 1 6 1 0.04
5401 Lukáš Strnad 1 1 6 1 0.04
5402 Malwina Kaźmierczyk 1 1 6 1 0.04
5403 Mannat Chopra 1 1 6 1 0.04
5404 Margaretha Sager 1 1 6 1 0.04
5405 Mary Powers 1 1 6 1 0.04
5406 Meredith Hamilton 1 1 6 1 0.04
5407 Michael Dickerson 1 1 6 1 0.04
5408 Michelle Eaton 1 1 6 1 0.04
5409 Miloslava Nováková 1 1 6 1 0.04
5410 Ming Gong 1 1 6 1 0.04
5411 Ming Lu 1 1 6 1 0.04
5412 Mitchell Flowers 1 1 6 1 0.04
5413 Mitchell Mcpherson 1 1 6 1 0.04
5414 Momoko Tanaka 1 1 6 1 0.04
5415 Nakul Samra 1 1 6 1 0.04
5416 Nanami Takahashi 1 1 6 1 0.04
5417 Nehmat Chaudhary 1 1 6 1 0.04
5418 Nicole Yoder 1 1 6 1 0.04
5419 Nitara Biswas 1 1 6 1 0.04
5420 Noah Braun 1 1 6 1 0.04
5421 Pari Chand 1 1 6 1 0.04
5422 Paul Harrell 1 1 6 1 0.04
5423 Ping Hu 1 1 6 1 0.04
5424 Ping Zheng 1 1 6 1 0.04
5425 Prerak Majumdar 1 1 6 1 0.04
5426 René Sans 1 1 6 1 0.04
5427 Richard Smith 1 1 6 1 0.04
5428 Rick Franklin 1 1 6 1 0.04
5429 Robert Fronia 1 1 6 1 0.04
5430 Robert Roberts 1 1 6 1 0.04
5431 Roman Švec 1 1 6 1 0.04
5432 Russell Barnes 1 1 6 1 0.04
5433 Sahil Tripathi 1 1 6 1 0.04
5434 Sarah Galloway 1 1 6 1 0.04
5435 Shari Willis 1 1 6 1 0.04
5436 Shelby Grimes 1 1 6 1 0.04
5437 Sophie Lebrun 1 1 6 1 0.04
5438 Stefan Zirme 1 1 6 1 0.04
5439 Steve Guzman 1 1 6 1 0.04
5440 Steven Schultz 1 1 6 1 0.04
5441 Szymon Pazik 1 1 6 1 0.04
5442 Tadeusz Gemza 1 1 6 1 0.04
5443 Terri Preston 1 1 6 1 0.04
5444 Theresa Mason 1 1 6 1 0.04
5445 Timothy Hansen 1 1 6 1 0.04
5446 Tracy Burton 1 1 6 1 0.04
5447 Tracy Ibarra 1 1 6 1 0.04
5448 Tracy Simpson 1 1 6 1 0.04
5449 Trevor Weiss 1 1 6 1 0.04
5450 Tyler Padilla 1 1 6 1 0.04
5451 Victor Spencer 1 1 6 1 0.04
5452 Victoria Johnson 1 1 6 1 0.04
5453 Viktor Švec 1 1 6 1 0.04
5454 Wigbert Scholl 1 1 6 1 0.04
5455 William Mcbride 1 1 6 1 0.04
5456 Xia Hou 1 1 6 1 0.04
5457 Yong Xie 1 1 6 1 0.04
5458 Yosuke Matsumoto 1 1 6 1 0.04
5459 Yui Watanabe 1 1 6 1 0.04
5460 Zara Anand 1 1 6 1 0.04
5461 Štěpánka Bártová 1 1 6 1 0.04
5462 Jeanne Perrin 1 1 2 2 0.04
5463 Marguerite Faure 1 1 2 2 0.04
5464 Alicia Kelly 1 1 9 0 0.04
5465 Brenda Stark 1 1 9 0 0.04
5466 Brittany Strickland 1 1 9 0 0.04
5467 Colleen Palmer 1 1 9 0 0.04
5468 Connie Estrada 1 1 9 0 0.04
5469 Danny Hill 1 1 9 0 0.04
5470 Denise Fisher 1 1 9 0 0.04
5471 Elizabeth Stone 1 1 9 0 0.04
5472 Frank Shaffer 1 1 9 0 0.04
5473 Gang Wu 1 1 9 0 0.04
5474 Guiying Wan 1 1 9 0 0.04
5475 Jacob Martin 1 1 9 0 0.04
5476 John Guzman II 1 1 9 0 0.04
5477 Jordan Thomas 1 1 9 0 0.04
5478 Joy Fletcher 1 1 9 0 0.04
5479 Karen Graham 1 1 9 0 0.04
5480 Katherine Bradley 1 1 9 0 0.04
5481 Kevin Rush 1 1 9 0 0.04
5482 Kimberly Collins 1 1 9 0 0.04
5483 Kimberly Jordan 1 1 9 0 0.04
5484 Kimberly Young 1 1 9 0 0.04
5485 Kristy Nelson 1 1 9 0 0.04
5486 Lara Horrocks 1 1 9 0 0.04
5487 Larry Jones 1 1 9 0 0.04
5488 Laura Rodriguez 1 1 9 0 0.04
5489 Lisa Nunez 1 1 9 0 0.04
5490 Lola Vermeulen 1 1 9 0 0.04
5491 Marc Andersen 1 1 9 0 0.04
5492 Mathew Schultz 1 1 9 0 0.04
5493 Matthew Hill 1 1 9 0 0.04
5494 Michelle Hensley 1 1 9 0 0.04
5495 Pamela Rollins 1 1 9 0 0.04
5496 Patricia Fisher 1 1 9 0 0.04
5497 Paul Lopez 1 1 9 0 0.04
5498 Peter Garcia 1 1 9 0 0.04
5499 Qiang Su 1 1 9 0 0.04
5500 Qiang Wen 1 1 9 0 0.04
5501 Robert Brown 1 1 9 0 0.04
5502 Rose Dean 1 1 9 0 0.04
5503 Samuel Ryan 1 1 9 0 0.04
5504 Sean Peck 1 1 9 0 0.04
5505 Sosimo Vila Bayona 1 1 9 0 0.04
5506 Travis Thompson 1 1 9 0 0.04
5507 Xiulan Xia 1 1 9 0 0.04
5508 Yong Xiong 1 1 9 0 0.04
5509 Adrianna Kurc 1 1 5 1 0.04
5510 Advika Ramakrishnan 1 1 5 1 0.04
5511 Alejandro Montenegro-Palomino 1 1 5 1 0.04
5512 Alexa Thomas 1 1 5 1 0.04
5513 Alonso del Benítez 1 1 5 1 0.04
5514 Amleto Fittipaldi 1 1 5 1 0.04
5515 Amy Mejia 1 1 5 1 0.04
5516 Anahi Sachdeva 1 1 5 1 0.04
5517 Anastazja Łuczyk 1 1 5 1 0.04
5518 Angela Hernandez 1 1 5 1 0.04
5519 Antonella Moschino 1 1 5 1 0.04
5520 Antonietta Chittolini 1 1 5 1 0.04
5521 April Welch 1 1 5 1 0.04
5522 Arsenio Berengario 1 1 5 1 0.04
5523 Aurélie de la Lucas 1 1 5 1 0.04
5524 Bethany Thompson 1 1 5 1 0.04
5525 Bianca Mentzel 1 1 5 1 0.04
5526 Birger Klemt 1 1 5 1 0.04
5527 Caleb Long 1 1 5 1 0.04
5528 Carlito Martí Jaume 1 1 5 1 0.04
5529 Carolyn Sharp 1 1 5 1 0.04
5530 Carsten Heinz 1 1 5 1 0.04
5531 Casey Scott 1 1 5 1 0.04
5532 Cassandra Zito 1 1 5 1 0.04
5533 Caterina Mercadante-Munari 1 1 5 1 0.04
5534 Cheryl White 1 1 5 1 0.04
5535 Chiyo Hashimoto 1 1 5 1 0.04
5536 Christina Martin 1 1 5 1 0.04
5537 Christine Olson 1 1 5 1 0.04
5538 Corey Stephens 1 1 5 1 0.04
5539 Craig Gonzalez 1 1 5 1 0.04
5540 Dagmara Pasztaleniec 1 1 5 1 0.04
5541 Daniel Harrington 1 1 5 1 0.04
5542 Danielle Ball 1 1 5 1 0.04
5543 Dawn Martinez 1 1 5 1 0.04
5544 Deanna Johnson 1 1 5 1 0.04
5545 Deanna Williams 1 1 5 1 0.04
5546 Debbie Hall 1 1 5 1 0.04
5547 Diana Lewis 1 1 5 1 0.04
5548 Ela Dara 1 1 5 1 0.04
5549 Elba Zaragoza-Doménech 1 1 5 1 0.04
5550 Eliana Marini 1 1 5 1 0.04
5551 Elizabeth Baker 1 1 5 1 0.04
5552 Elizabeth Brown 1 1 5 1 0.04
5553 Elizabeth Webb 1 1 5 1 0.04
5554 Ernestine Franke-Kade 1 1 5 1 0.04
5555 Eugenia Lindner 1 1 5 1 0.04
5556 Fang Zeng 1 1 5 1 0.04
5557 Gabriel Silva 1 1 5 1 0.04
5558 Garrett Salazar 1 1 5 1 0.04
5559 George Snyder 1 1 5 1 0.04
5560 Gesine Gehringer-Rogner 1 1 5 1 0.04
5561 Giampiero Lombroso 1 1 5 1 0.04
5562 Gianfranco Trentini 1 1 5 1 0.04
5563 Gianluca Barberini 1 1 5 1 0.04
5564 Gordana Schwital 1 1 5 1 0.04
5565 Gregory Huffman 1 1 5 1 0.04
5566 Griselda Renier-Mozart 1 1 5 1 0.04
5567 Hans-Willi Henschel 1 1 5 1 0.04
5568 Hansh Dyal 1 1 5 1 0.04
5569 Heinz-Georg Segebahn 1 1 5 1 0.04
5570 Henrike Pärtzelt 1 1 5 1 0.04
5571 Herminia Vazquez-Sainz 1 1 5 1 0.04
5572 Herr Detlef Geißler 1 1 5 1 0.04
5573 Herr Ulrich Gieß 1 1 5 1 0.04
5574 Hidde Huisman 1 1 5 1 0.04
5575 Indrajit Borra 1 1 5 1 0.04
5576 Isaac Joubert 1 1 5 1 0.04
5577 Itziar Mata Marqués 1 1 5 1 0.04
5578 Ivana Bains 1 1 5 1 0.04
5579 Iwo Olbrych 1 1 5 1 0.04
5580 Iwo Zbrzeźniak 1 1 5 1 0.04
5581 James Bauer 1 1 5 1 0.04
5582 James Munoz 1 1 5 1 0.04
5583 Janina Mazik 1 1 5 1 0.04
5584 Jared Banks 1 1 5 1 0.04
5585 Jasmine Thompson 1 1 5 1 0.04
5586 Jason Baker 1 1 5 1 0.04
5587 Jayan Chandran 1 1 5 1 0.04
5588 Jeremi Kwarciak 1 1 5 1 0.04
5589 Jeremy Hahn 1 1 5 1 0.04
5590 Jessica Parsons 1 1 5 1 0.04
5591 Jie Zeng 1 1 5 1 0.04
5592 Joann Smith 1 1 5 1 0.04
5593 Joel Miller 1 1 5 1 0.04
5594 Johannes de Vries 1 1 5 1 0.04
5595 Jolanta Schlosser 1 1 5 1 0.04
5596 Jonathan May 1 1 5 1 0.04
5597 Jorge Ross 1 1 5 1 0.04
5598 Jose Sanders 1 1 5 1 0.04
5599 Joseph Wheeler 1 1 5 1 0.04
5600 Julie Lane 1 1 5 1 0.04
5601 Józef Niewiara 1 1 5 1 0.04
5602 Kalina Fidor 1 1 5 1 0.04
5603 Katherine Wang 1 1 5 1 0.04
5604 Kathleen Russo 1 1 5 1 0.04
5605 Kelly Sanders 1 1 5 1 0.04
5606 Klara Betlej 1 1 5 1 0.04
5607 Kornelia Raczkiewicz 1 1 5 1 0.04
5608 Krista Rogge 1 1 5 1 0.04
5609 Kristina Taylor 1 1 5 1 0.04
5610 Leocadia Falcón Diez 1 1 5 1 0.04
5611 Lucia Corradi 1 1 5 1 0.04
5612 Manikya Badal 1 1 5 1 0.04
5613 Manon Voisin 1 1 5 1 0.04
5614 Marcelina Kosno 1 1 5 1 0.04
5615 Mateusz Perczak 1 1 5 1 0.04
5616 Matthew Hall 1 1 5 1 0.04
5617 Matthew Murphy 1 1 5 1 0.04
5618 Maurycy Bohdanowicz 1 1 5 1 0.04
5619 Mechthilde Kühnert 1 1 5 1 0.04
5620 Megan Mcclure 1 1 5 1 0.04
5621 Melissa Soto 1 1 5 1 0.04
5622 Michael Keller 1 1 5 1 0.04
5623 Michael Thompson 1 1 5 1 0.04
5624 Micheal Martin 1 1 5 1 0.04
5625 Michel Pichon 1 1 5 1 0.04
5626 Michelle Barnes 1 1 5 1 0.04
5627 Nacio Joel Corral Domingo 1 1 5 1 0.04
5628 Natasha Duncan 1 1 5 1 0.04
5629 Nathaniel Alvarado 1 1 5 1 0.04
5630 Nicola Napolitano 1 1 5 1 0.04
5631 Nicole Bowers 1 1 5 1 0.04
5632 Nicole Mccormick 1 1 5 1 0.04
5633 Nina Pareto 1 1 5 1 0.04
5634 Nishith Sachar 1 1 5 1 0.04
5635 Nitya Dass 1 1 5 1 0.04
5636 Nout Zijlmans 1 1 5 1 0.04
5637 Nynke Corstiaens 1 1 5 1 0.04
5638 Octavia Gilabert León 1 1 5 1 0.04
5639 Olivia Solomon 1 1 5 1 0.04
5640 Onkar Shankar 1 1 5 1 0.04
5641 Osamu Goto 1 1 5 1 0.04
5642 Pam Cunningham 1 1 5 1 0.04
5643 Paola Täsche 1 1 5 1 0.04
5644 Patrick Geisler 1 1 5 1 0.04
5645 Patrick Nguyen 1 1 5 1 0.04
5646 Perry Lopez 1 1 5 1 0.04
5647 Peter Collins 1 1 5 1 0.04
5648 Ping Long 1 1 5 1 0.04
5649 Piotr Wacław 1 1 5 1 0.04
5650 Pranay Mahajan 1 1 5 1 0.04
5651 Rafał Raba 1 1 5 1 0.04
5652 Rhonda Mcgee 1 1 5 1 0.04
5653 Rika Yamamoto 1 1 5 1 0.04
5654 Robert Cannon 1 1 5 1 0.04
5655 Robert King 1 1 5 1 0.04
5656 Robert Pugh 1 1 5 1 0.04
5657 Roksana Kurzyna 1 1 5 1 0.04
5658 Sara Jover Iglesias 1 1 5 1 0.04
5659 Sarah Ward 1 1 5 1 0.04
5660 Sean Rush 1 1 5 1 0.04
5661 Sebastiano Torlonia 1 1 5 1 0.04
5662 Segismundo Hoyos Múgica 1 1 5 1 0.04
5663 Sharon Bray 1 1 5 1 0.04
5664 Sharon Mcclain 1 1 5 1 0.04
5665 Shayak Chakraborty 1 1 5 1 0.04
5666 Sheila Cox 1 1 5 1 0.04
5667 Sheri Calhoun 1 1 5 1 0.04
5668 Sibylla Beyer 1 1 5 1 0.04
5669 Sibylla Heintze 1 1 5 1 0.04
5670 Susanne Pierre 1 1 5 1 0.04
5671 Tammy Wilcox 1 1 5 1 0.04
5672 Terry Clark 1 1 5 1 0.04
5673 Timothy Lopez 1 1 5 1 0.04
5674 Tiya Kota 1 1 5 1 0.04
5675 Umang Uppal 1 1 5 1 0.04
5676 William de la Valentin 1 1 5 1 0.04
5677 Xavier Pierre 1 1 5 1 0.04
5678 Yoko Nishimura 1 1 5 1 0.04
5679 Yong Huang 1 1 5 1 0.04
5680 Yong Ma 1 1 5 1 0.04
5681 Yong Yin 1 1 5 1 0.04
5682 Zacharie Morin 1 1 5 1 0.04
5683 Zara Choudhury 1 1 5 1 0.04
5684 Zeeshan Kuruvilla 1 1 5 1 0.04
5685 Aaron Medina 1 1 8 0 0.04
5686 Alfonso Bähr 1 1 8 0 0.04
5687 Andrew Ellison 1 1 8 0 0.04
5688 Annelise Berger 1 1 8 0 0.04
5689 Aurélie Traore de Grondin 1 1 8 0 0.04
5690 Billy Martinez 1 1 8 0 0.04
5691 Brian Mathis 1 1 8 0 0.04
5692 Brian Richard 1 1 8 0 0.04
5693 Candice Avila 1 1 8 0 0.04
5694 Carl Brown 1 1 8 0 0.04
5695 Carrie Smith 1 1 8 0 0.04
5696 Charles Thomas 1 1 8 0 0.04
5697 Curtis Cooper 1 1 8 0 0.04
5698 Deanna Green 1 1 8 0 0.04
5699 Douglas Gray 1 1 8 0 0.04
5700 Drew Green 1 1 8 0 0.04
5701 Edward Gutierrez 1 1 8 0 0.04
5702 Gang Qiu 1 1 8 0 0.04
5703 Gregory Morgan 1 1 8 0 0.04
5704 Gudula Mitschke-Reuter 1 1 8 0 0.04
5705 Henry Ortega 1 1 8 0 0.04
5706 Holly Garcia 1 1 8 0 0.04
5707 Hubert Oderwald 1 1 8 0 0.04
5708 Hélène Lévêque 1 1 8 0 0.04
5709 Isaiah Gonzales 1 1 8 0 0.04
5710 Jason Lewis 1 1 8 0 0.04
5711 Jennifer Acosta 1 1 8 0 0.04
5712 Jeremy Watkins 1 1 8 0 0.04
5713 John Brooks 1 1 8 0 0.04
5714 Joshua Holmes 1 1 8 0 0.04
5715 Justin Carroll 1 1 8 0 0.04
5716 Karen Munoz 1 1 8 0 0.04
5717 Katie Smith 1 1 8 0 0.04
5718 Kimberly Foster 1 1 8 0 0.04
5719 Kimberly Kim 1 1 8 0 0.04
5720 Kristin Thomas 1 1 8 0 0.04
5721 Laura Howard 1 1 8 0 0.04
5722 Laura Johnson 1 1 8 0 0.04
5723 Lilo Christoph 1 1 8 0 0.04
5724 Lucas Jose Manuel Arnaiz Pinedo 1 1 8 0 0.04
5725 Maggie-Margot Vallée 1 1 8 0 0.04
5726 Mary Turner 1 1 8 0 0.04
5727 Megan Fox 1 1 8 0 0.04
5728 Michael Gibbs 1 1 8 0 0.04
5729 Miguel Shelton 1 1 8 0 0.04
5730 Nicole Anderson 1 1 8 0 0.04
5731 Olivier Munoz 1 1 8 0 0.04
5732 Paul Williams 1 1 8 0 0.04
5733 Phillip Stone 1 1 8 0 0.04
5734 Qiang Jin 1 1 8 0 0.04
5735 Rachel Hogan 1 1 8 0 0.04
5736 Ramona Petruzzi 1 1 8 0 0.04
5737 Raven Bowman 1 1 8 0 0.04
5738 Rebecca Ramirez 1 1 8 0 0.04
5739 Sara Estefanía Acedo Caro 1 1 8 0 0.04
5740 Sean Walters 1 1 8 0 0.04
5741 Steven Harris 1 1 8 0 0.04
5742 Tara Ortiz 1 1 8 0 0.04
5743 Timothy Beck 1 1 8 0 0.04
5744 Tracy Camacho 1 1 8 0 0.04
5745 William Peck 1 1 8 0 0.04
5746 Xiulan Kong 1 1 8 0 0.04
5747 Xiuying Gao 1 1 8 0 0.04
5748 Leo Hamilton 2 0 5 1 0.04
5749 Noah Bennett 2 0 5 1 0.04
5750 Aaron Martinez 1 1 4 1 0.04
5751 Adriana Trobbiani 1 1 4 1 0.04
5752 Advik Singh 1 1 4 1 0.04
5753 Akira Kobayashi 1 1 4 1 0.04
5754 Alicia Smith 1 1 4 1 0.04
5755 Alison Armstrong 1 1 4 1 0.04
5756 Allison Harris 1 1 4 1 0.04
5757 Andrew Barnes 1 1 4 1 0.04
5758 Angela Greene 1 1 4 1 0.04
5759 Ann Anderson 1 1 4 1 0.04
5760 Anouk Rivière 1 1 4 1 0.04
5761 Anthony Erickson 1 1 4 1 0.04
5762 April Crosby 1 1 4 1 0.04
5763 April Ortiz 1 1 4 1 0.04
5764 Ben le Gallen 1 1 4 1 0.04
5765 Benjamin Rogers 1 1 4 1 0.04
5766 Blake Melton 1 1 4 1 0.04
5767 Brandon Trujillo 1 1 4 1 0.04
5768 Brandon Williams 1 1 4 1 0.04
5769 Dale Stewart 1 1 4 1 0.04
5770 David Smith 1 1 4 1 0.04
5771 Deborah White 1 1 4 1 0.04
5772 Donna Anderson 1 1 4 1 0.04
5773 Douglas Stout 1 1 4 1 0.04
5774 Edward Ellis 1 1 4 1 0.04
5775 Elizabeth Phillips 1 1 4 1 0.04
5776 Fang Ye 1 1 4 1 0.04
5777 Farhan Bansal 1 1 4 1 0.04
5778 Franziska Lindau 1 1 4 1 0.04
5779 Gerold Sontag 1 1 4 1 0.04
5780 Graziella Marenzio-Scialpi 1 1 4 1 0.04
5781 Guiying Wen 1 1 4 1 0.04
5782 Heidelore Meister 1 1 4 1 0.04
5783 Herr Alexander Eckbauer 1 1 4 1 0.04
5784 Holly Lucero 1 1 4 1 0.04
5785 Jacqueline Hörle 1 1 4 1 0.04
5786 James Copeland 1 1 4 1 0.04
5787 Jessica Bernard 1 1 4 1 0.04
5788 Joan Brown 1 1 4 1 0.04
5789 Jocelyn Hall 1 1 4 1 0.04
5790 John Carlson 1 1 4 1 0.04
5791 Joseph Smith 1 1 4 1 0.04
5792 Joyce Norris 1 1 4 1 0.04
5793 Kashvi Dutt 1 1 4 1 0.04
5794 Katrina Guzman 1 1 4 1 0.04
5795 Kayla Grimes 1 1 4 1 0.04
5796 Kimaya Aggarwal 1 1 4 1 0.04
5797 Krish Thaker 1 1 4 1 0.04
5798 Kyle Johnson 1 1 4 1 0.04
5799 Leonie Kabus 1 1 4 1 0.04
5800 Leslie Page 1 1 4 1 0.04
5801 Lindsey Jackson 1 1 4 1 0.04
5802 Lucas Aguilar 1 1 4 1 0.04
5803 Maik Eckbauer 1 1 4 1 0.04
5804 Maria Priuli 1 1 4 1 0.04
5805 Marianna Drózd 1 1 4 1 0.04
5806 Mariola Häring 1 1 4 1 0.04
5807 Mark Schmidt 1 1 4 1 0.04
5808 Martin Frederick 1 1 4 1 0.04
5809 Mary Freeman 1 1 4 1 0.04
5810 Mary Gutierrez 1 1 4 1 0.04
5811 Matthew James 1 1 4 1 0.04
5812 Micha Weinhage 1 1 4 1 0.04
5813 Michael Wise 1 1 4 1 0.04
5814 Michael Wright 1 1 4 1 0.04
5815 Michelle Caldwell 1 1 4 1 0.04
5816 Miki Maeda 1 1 4 1 0.04
5817 Nelly Austermühle 1 1 4 1 0.04
5818 Nisa van de Biezenbos 1 1 4 1 0.04
5819 Nomadic Resonance 1 1 4 1 0.04
5820 Olivie Sauvage 1 1 4 1 0.04
5821 Olivier Antoine 1 1 4 1 0.04
5822 Pamela Griffin 1 1 4 1 0.04
5823 Paola Scheuermann 1 1 4 1 0.04
5824 Patrick Bertin 1 1 4 1 0.04
5825 Patrick Lindsey 1 1 4 1 0.04
5826 Pihu Ahuja 1 1 4 1 0.04
5827 Prisha Varkey 1 1 4 1 0.04
5828 Rebecca Austin 1 1 4 1 0.04
5829 Rebecca Webb 1 1 4 1 0.04
5830 Rhea Bajwa 1 1 4 1 0.04
5831 Rhonda Knox 1 1 4 1 0.04
5832 Riaan Balan 1 1 4 1 0.04
5833 Robert Jennings 1 1 4 1 0.04
5834 Rodney Moss 1 1 4 1 0.04
5835 Russell Ruiz 1 1 4 1 0.04
5836 Samar Sarma 1 1 4 1 0.04
5837 Sarah Hayes 1 1 4 1 0.04
5838 Scott Villa 1 1 4 1 0.04
5839 Shalv Dass 1 1 4 1 0.04
5840 Sophie Broeckx 1 1 4 1 0.04
5841 Théophile de Carlier 1 1 4 1 0.04
5842 Valeska Heinrich 1 1 4 1 0.04
5843 Vanessa King 1 1 4 1 0.04
5844 Virginia White 1 1 4 1 0.04
5845 William Burton 1 1 4 1 0.04
5846 Xiulan Han 1 1 4 1 0.04
5847 Yara Robbrechts Bruijne 1 1 4 1 0.04
5848 Zaina Zacharia 1 1 4 1 0.04
5849 Christine Robbins 1 0 17 2 0.04
5850 Dennis Hill PhD 1 0 17 2 0.04
5851 Amy Gonzalez 1 1 7 0 0.04
5852 Annunziata Disdero 1 1 7 0 0.04
5853 Brian Nguyen 1 1 7 0 0.04
5854 Bryan Matthews 1 1 7 0 0.04
5855 Bryan Shaw 1 1 7 0 0.04
5856 Camilla Zaccagnini-Bertoni 1 1 7 0 0.04
5857 Carlos Cannon 1 1 7 0 0.04
5858 Carrie Perry 1 1 7 0 0.04
5859 Catherine Black 1 1 7 0 0.04
5860 Christina Robinson 1 1 7 0 0.04
5861 Christopher Romero 1 1 7 0 0.04
5862 Diana Trujillo 1 1 7 0 0.04
5863 Emilio Farnese 1 1 7 0 0.04
5864 Fortunata Zeffirelli 1 1 7 0 0.04
5865 Francisco Campbell 1 1 7 0 0.04
5866 Friedbert Stey 1 1 7 0 0.04
5867 Gary Wallace 1 1 7 0 0.04
5868 Gina Cooper 1 1 7 0 0.04
5869 Giustino Carocci 1 1 7 0 0.04
5870 Guiying Li 1 1 7 0 0.04
5871 Hannah Payne 1 1 7 0 0.04
5872 Hannah Richards 1 1 7 0 0.04
5873 Hans-H. Jacobi Jäckel 1 1 7 0 0.04
5874 Harold Gutierrez 1 1 7 0 0.04
5875 Isabella Cook 1 1 7 0 0.04
5876 Jacopo Vianello 1 1 7 0 0.04
5877 Jesus Rivas 1 1 7 0 0.04
5878 Jie Yu 1 1 7 0 0.04
5879 Jill Goodman 1 1 7 0 0.04
5880 Job Mahieu 1 1 7 0 0.04
5881 Jonathan Horne 1 1 7 0 0.04
5882 Joseph Maxwell 1 1 7 0 0.04
5883 Juan Cui 1 1 7 0 0.04
5884 Juan Xie 1 1 7 0 0.04
5885 Kayla Taylor 1 1 7 0 0.04
5886 Kim Gonzalez 1 1 7 0 0.04
5887 Lara Scaramucci-Santi 1 1 7 0 0.04
5888 Laura Ward 1 1 7 0 0.04
5889 Laura Wolf 1 1 7 0 0.04
5890 Linda Ward 1 1 7 0 0.04
5891 Lindsay Marsh 1 1 7 0 0.04
5892 Marc Drake 1 1 7 0 0.04
5893 Maria Merritt 1 1 7 0 0.04
5894 Mary Griffin 1 1 7 0 0.04
5895 Matthew Gardner 1 1 7 0 0.04
5896 Mattia Pepe 1 1 7 0 0.04
5897 Michael Gordon 1 1 7 0 0.04
5898 Monica Dean 1 1 7 0 0.04
5899 Morgan Brown 1 1 7 0 0.04
5900 Na Xiong 1 1 7 0 0.04
5901 Na Xue 1 1 7 0 0.04
5902 Phillip Jones 1 1 7 0 0.04
5903 Raymond Ponce 1 1 7 0 0.04
5904 Rebecca Ruiz 1 1 7 0 0.04
5905 Robert Anderson 1 1 7 0 0.04
5906 Ryan Blevins 1 1 7 0 0.04
5907 Samantha Snow 1 1 7 0 0.04
5908 Sarah Wilson 1 1 7 0 0.04
5909 Shannon Bennett 1 1 7 0 0.04
5910 Shannon Romero 1 1 7 0 0.04
5911 Sharon Grant 1 1 7 0 0.04
5912 Sharon Parrish 1 1 7 0 0.04
5913 Steven Cordova 1 1 7 0 0.04
5914 Steven Keller 1 1 7 0 0.04
5915 Susan Meadows 1 1 7 0 0.04
5916 Tao Fu 1 1 7 0 0.04
5917 Tao Xia 1 1 7 0 0.04
5918 Thomas Gray 1 1 7 0 0.04
5919 Valerie Matthews 1 1 7 0 0.04
5920 Valerie Navarro 1 1 7 0 0.04
5921 Xiuying Jiang 1 1 7 0 0.04
5922 Yang Sun 1 1 7 0 0.04
5923 Adam Harmon 1 1 3 1 0.04
5924 Angela Wolfe 1 1 3 1 0.04
5925 Annette Collins 1 1 3 1 0.04
5926 Ashley Castillo 1 1 3 1 0.04
5927 Bianka Tabak 1 1 3 1 0.04
5928 Caitlin Allen 1 1 3 1 0.04
5929 Calogero Zamorani 1 1 3 1 0.04
5930 Chelsea Mclean 1 1 3 1 0.04
5931 Chloe Little 1 1 3 1 0.04
5932 Christie Woods 1 1 3 1 0.04
5933 Christopher Humphrey 1 1 3 1 0.04
5934 Costantino Ramazzotti 1 1 3 1 0.04
5935 Dagmara Smorąg 1 1 3 1 0.04
5936 Elizabeth Hill 1 1 3 1 0.04
5937 Elvira Puente Alba 1 1 3 1 0.04
5938 Emily Campos 1 1 3 1 0.04
5939 Enzo Morlacchi-Grassi 1 1 3 1 0.04
5940 Fernanda Carocci 1 1 3 1 0.04
5941 Gabrielle-Susan Bousquet 1 1 3 1 0.04
5942 Gwendolyn Morgan 1 1 3 1 0.04
5943 Haley Lawson 1 1 3 1 0.04
5944 Haley Silva 1 1 3 1 0.04
5945 Henriette Pierre 1 1 3 1 0.04
5946 James Medina 1 1 3 1 0.04
5947 Janet Parks 1 1 3 1 0.04
5948 Jason Owen 1 1 3 1 0.04
5949 Jennifer Reese 1 1 3 1 0.04
5950 Jeremy Martinez 1 1 3 1 0.04
5951 Jillian Crane 1 1 3 1 0.04
5952 John Holt 1 1 3 1 0.04
5953 Jon Harris 1 1 3 1 0.04
5954 Jonathan Fisher 1 1 3 1 0.04
5955 Juliusz Kominiak 1 1 3 1 0.04
5956 Kenneth Maldonado 1 1 3 1 0.04
5957 Kevin Wells 1 1 3 1 0.04
5958 Kimberly Fernandez 1 1 3 1 0.04
5959 Klaus-Werner Gierschner 1 1 3 1 0.04
5960 Lara Filippelli 1 1 3 1 0.04
5961 Lawrence Larson 1 1 3 1 0.04
5962 Leslie Hinton 1 1 3 1 0.04
5963 Linda Burns 1 1 3 1 0.04
5964 Lindsay Browning 1 1 3 1 0.04
5965 Lindsey Broeshart 1 1 3 1 0.04
5966 Ludovico Sgarbi-Interminei 1 1 3 1 0.04
5967 Marcella Dobes 1 1 3 1 0.04
5968 Margherita Montanari 1 1 3 1 0.04
5969 Mariusz Segebahn 1 1 3 1 0.04
5970 Martin Jones PhD 1 1 3 1 0.04
5971 Matthew Best 1 1 3 1 0.04
5972 Maurizio Curatoli 1 1 3 1 0.04
5973 Megan Humphrey 1 1 3 1 0.04
5974 Michael Wilson 1 1 3 1 0.04
5975 Miss Kathleen Price 1 1 3 1 0.04
5976 Natalija Täsche 1 1 3 1 0.04
5977 Nathalie Striebitz 1 1 3 1 0.04
5978 Nicole Newman 1 1 3 1 0.04
5979 Noah Shaw 1 1 3 1 0.04
5980 Olaf Dymarczyk 1 1 3 1 0.04
5981 Orlando Garzoni 1 1 3 1 0.04
5982 Patrick Walsh 1 1 3 1 0.04
5983 Paul-Robert Bodin 1 1 3 1 0.04
5984 Pedro Mitschke 1 1 3 1 0.04
5985 Pieter van Dijk 1 1 3 1 0.04
5986 Rachel Hall 1 1 3 1 0.04
5987 Robert Stevenson 1 1 3 1 0.04
5988 Roger Walsh 1 1 3 1 0.04
5989 Rosario Alsina Saez 1 1 3 1 0.04
5990 Samantha Campbell 1 1 3 1 0.04
5991 Samuel Graham 1 1 3 1 0.04
5992 Sara Roman 1 1 3 1 0.04
5993 Sean Blackburn 1 1 3 1 0.04
5994 Shannon Thompson 1 1 3 1 0.04
5995 Sheila Acosta 1 1 3 1 0.04
5996 Shelby Hunt 1 1 3 1 0.04
5997 Sheri Hernandez 1 1 3 1 0.04
5998 Stephanie Bird 1 1 3 1 0.04
5999 Tammy Craig 1 1 3 1 0.04
6000 Thomas Clark 1 1 3 1 0.04
6001 Timothy Randolph 1 1 3 1 0.04
6002 Tracy Williamson 1 1 3 1 0.04
6003 Wesley Cain 1 1 3 1 0.04
6004 Wieslaw Heydrich 1 1 3 1 0.04
6005 William Blair 1 1 3 1 0.04
6006 Yong Xiang 1 1 3 1 0.04
6007 Zachary Rojas 1 1 3 1 0.04
6008 Aaron Horton 1 1 6 0 0.04
6009 Aarush Shukla 1 1 6 0 0.04
6010 Adam Hinton 1 1 6 0 0.04
6011 Agnolo Zaccardo-Leonetti 1 1 6 0 0.04
6012 Alberto Durante-Depero 1 1 6 0 0.04
6013 Alyssa Franklin 1 1 6 0 0.04
6014 Amy Conway 1 1 6 0 0.04
6015 Amy Thomas 1 1 6 0 0.04
6016 Anay Vohra 1 1 6 0 0.04
6017 Anja Niemeier 1 1 6 0 0.04
6018 Anthony Lane 1 1 6 0 0.04
6019 Aradhya Maharaj 1 1 6 0 0.04
6020 Armida Luque Mascaró 1 1 6 0 0.04
6021 Arnaldo Borzomì 1 1 6 0 0.04
6022 Ashley Logan 1 1 6 0 0.04
6023 Ashley Osborne 1 1 6 0 0.04
6024 Aurora Alboni 1 1 6 0 0.04
6025 Brandon Cohen 1 1 6 0 0.04
6026 Brandon Morales 1 1 6 0 0.04
6027 Brian Jones 1 1 6 0 0.04
6028 Briana Miller 1 1 6 0 0.04
6029 Calvin Cooper 1 1 6 0 0.04
6030 Carl Rodriguez 1 1 6 0 0.04
6031 Carmen Higgins 1 1 6 0 0.04
6032 Casey Wright 1 1 6 0 0.04
6033 Cemal Dowerg 1 1 6 0 0.04
6034 Chao Du 1 1 6 0 0.04
6035 Chao Ma 1 1 6 0 0.04
6036 Charles Marshall 1 1 6 0 0.04
6037 Charles Snyder 1 1 6 0 0.04
6038 Christina Soto 1 1 6 0 0.04
6039 Christopher Flores 1 1 6 0 0.04
6040 Christopher Jones 1 1 6 0 0.04
6041 Christopher Lowery 1 1 6 0 0.04
6042 Christopher Pham 1 1 6 0 0.04
6043 Concetta Davids 1 1 6 0 0.04
6044 Coriolano Riccati 1 1 6 0 0.04
6045 Craig Lewis 1 1 6 0 0.04
6046 Cristian Gross 1 1 6 0 0.04
6047 Cynthia Henson 1 1 6 0 0.04
6048 Danny Brooks 1 1 6 0 0.04
6049 David Cannon 1 1 6 0 0.04
6050 David Mayo 1 1 6 0 0.04
6051 Dawn Allen 1 1 6 0 0.04
6052 Dennis Bass 1 1 6 0 0.04
6053 Devin Wallace 1 1 6 0 0.04
6054 Diana White 1 1 6 0 0.04
6055 Diane Brooks 1 1 6 0 0.04
6056 Diane Segebahn 1 1 6 0 0.04
6057 Domenico Nette 1 1 6 0 0.04
6058 Doris Becker 1 1 6 0 0.04
6059 Ebony Acevedo 1 1 6 0 0.04
6060 Eddie Taylor 1 1 6 0 0.04
6061 Edward Parker 1 1 6 0 0.04
6062 Edwin Crosby 1 1 6 0 0.04
6063 Ela Batra 1 1 6 0 0.04
6064 Elizabeth Davis 1 1 6 0 0.04
6065 Elizabeth Spence 1 1 6 0 0.04
6066 Emily Gonzalez 1 1 6 0 0.04
6067 Eva Morosini-Procacci 1 1 6 0 0.04
6068 Eva-Maria Killer 1 1 6 0 0.04
6069 Fang Cheng 1 1 6 0 0.04
6070 Farhan Bava 1 1 6 0 0.04
6071 Franco Weitzel 1 1 6 0 0.04
6072 Gang Fang 1 1 6 0 0.04
6073 Gregory Rhodes 1 1 6 0 0.04
6074 Guiying Cheng 1 1 6 0 0.04
6075 Guiying Wang 1 1 6 0 0.04
6076 Gunhild Oderwald 1 1 6 0 0.04
6077 Hanako Suzuki 1 1 6 0 0.04
6078 Hansjoachim Gieß 1 1 6 0 0.04
6079 Heather Pollard 1 1 6 0 0.04
6080 Hector Smith 1 1 6 0 0.04
6081 Heidi Henry 1 1 6 0 0.04
6082 Heidi Mills 1 1 6 0 0.04
6083 Henriette Duval 1 1 6 0 0.04
6084 Herr Karl-Josef Roskoth 1 1 6 0 0.04
6085 Imran van Maasgouw-Schroeff 1 1 6 0 0.04
6086 Isis van Tours-Kathagen 1 1 6 0 0.04
6087 Jack Hawkins 1 1 6 0 0.04
6088 Jacob Garcia 1 1 6 0 0.04
6089 Jacob Orr 1 1 6 0 0.04
6090 Jacqueline Obrien 1 1 6 0 0.04
6091 James Cooper 1 1 6 0 0.04
6092 James Nolan 1 1 6 0 0.04
6093 Jason Mayer 1 1 6 0 0.04
6094 Jeffrey Lee 1 1 6 0 0.04
6095 Jeffrey Miller 1 1 6 0 0.04
6096 Jennifer Norris 1 1 6 0 0.04
6097 Jennifer Parker 1 1 6 0 0.04
6098 Jermaine York 1 1 6 0 0.04
6099 Jing Fu 1 1 6 0 0.04
6100 John Bates 1 1 6 0 0.04
6101 John Hopkins 1 1 6 0 0.04
6102 John Howe 1 1 6 0 0.04
6103 Jonathan Snyder 1 1 6 0 0.04
6104 Joseph Navarro 1 1 6 0 0.04
6105 Joseph Rey 1 1 6 0 0.04
6106 Jun Okada 1 1 6 0 0.04
6107 Jérôme Blanchet 1 1 6 0 0.04
6108 Karen White 1 1 6 0 0.04
6109 Kari Harvey 1 1 6 0 0.04
6110 Kathryn Garcia 1 1 6 0 0.04
6111 Kayla Holmes 1 1 6 0 0.04
6112 Kelly Jackson 1 1 6 0 0.04
6113 Kevin Clark 1 1 6 0 0.04
6114 Kimaya Srinivasan 1 1 6 0 0.04
6115 Kimberly Buchanan 1 1 6 0 0.04
6116 Kristina Jackson 1 1 6 0 0.04
6117 Lei Tian 1 1 6 0 0.04
6118 Letizia Agnesi-Piacentini 1 1 6 0 0.04
6119 Liesel Aumann 1 1 6 0 0.04
6120 Linda Garza 1 1 6 0 0.04
6121 Mandy Mendoza 1 1 6 0 0.04
6122 Manuel Murphy 1 1 6 0 0.04
6123 Marc Arroyo 1 1 6 0 0.04
6124 Marco Kusch 1 1 6 0 0.04
6125 Mark Shepard 1 1 6 0 0.04
6126 Martin Ramirez 1 1 6 0 0.04
6127 Mary Gorlitz 1 1 6 0 0.04
6128 Mary Harris 1 1 6 0 0.04
6129 Mary Lewis 1 1 6 0 0.04
6130 Matthew Lopez 1 1 6 0 0.04
6131 Matthew Mclean 1 1 6 0 0.04
6132 Matthew Scott 1 1 6 0 0.04
6133 Maurice Lacroix 1 1 6 0 0.04
6134 Melissa Keller 1 1 6 0 0.04
6135 Melissa Matthews 1 1 6 0 0.04
6136 Melissa Moore 1 1 6 0 0.04
6137 Michael Mitchell 1 1 6 0 0.04
6138 Michael Washington 1 1 6 0 0.04
6139 Micheal Collins 1 1 6 0 0.04
6140 Michele Johnson 1 1 6 0 0.04
6141 Michelle Blair 1 1 6 0 0.04
6142 Ming Ma 1 1 6 0 0.04
6143 Minoru Ikeda 1 1 6 0 0.04
6144 Miraan Ramachandran 1 1 6 0 0.04
6145 Morgan Rodriguez 1 1 6 0 0.04
6146 Na Fang 1 1 6 0 0.04
6147 Na Jiang 1 1 6 0 0.04
6148 Na Shen 1 1 6 0 0.04
6149 Na Zheng 1 1 6 0 0.04
6150 Nathan Carrillo 1 1 6 0 0.04
6151 Nichole Cruz 1 1 6 0 0.04
6152 Nicole Jackson 1 1 6 0 0.04
6153 Nicole Lamb 1 1 6 0 0.04
6154 Noël Loiseau 1 1 6 0 0.04
6155 Omar Lewis 1 1 6 0 0.04
6156 Onkar Kata 1 1 6 0 0.04
6157 Ottavio Barcella-Ritacca 1 1 6 0 0.04
6158 Paolo Gotthard 1 1 6 0 0.04
6159 Patrick Lewis 1 1 6 0 0.04
6160 Patrick Simon 1 1 6 0 0.04
6161 Paul Brown 1 1 6 0 0.04
6162 Pierluigi Tornatore 1 1 6 0 0.04
6163 Ping Wei 1 1 6 0 0.04
6164 Ping Zou 1 1 6 0 0.04
6165 Pomponio Pometta 1 1 6 0 0.04
6166 Pranay Sarin 1 1 6 0 0.04
6167 Qiang Chen 1 1 6 0 0.04
6168 Rachel Carroll 1 1 6 0 0.04
6169 Rania Deol 1 1 6 0 0.04
6170 Rebecca Marshall 1 1 6 0 0.04
6171 Renée Tanguy-Henry 1 1 6 0 0.04
6172 Ritvik Lanka 1 1 6 0 0.04
6173 Robert Santiago 1 1 6 0 0.04
6174 Robert Shaw 1 1 6 0 0.04
6175 Ryan Devan 1 1 6 0 0.04
6176 Ryan Roberts 1 1 6 0 0.04
6177 Serafina Vives-Somoza 1 1 6 0 0.04
6178 Shannon Allen 1 1 6 0 0.04
6179 Siegmar Heintze 1 1 6 0 0.04
6180 Silvia Baum 1 1 6 0 0.04
6181 Stephen Decker 1 1 6 0 0.04
6182 Susan Holmes 1 1 6 0 0.04
6183 Tami Cox 1 1 6 0 0.04
6184 Tammy Turner 1 1 6 0 0.04
6185 Tammy Williams 1 1 6 0 0.04
6186 Tanya Webb 1 1 6 0 0.04
6187 Tao Wan 1 1 6 0 0.04
6188 Tara Kota 1 1 6 0 0.04
6189 Teresa Ellis 1 1 6 0 0.04
6190 Thibault Ollivier 1 1 6 0 0.04
6191 Thomas Alvarez 1 1 6 0 0.04
6192 Thomas Le 1 1 6 0 0.04
6193 Tina Cook 1 1 6 0 0.04
6194 Tiya Sani 1 1 6 0 0.04
6195 Tracy Jones 1 1 6 0 0.04
6196 Tracy Wright 1 1 6 0 0.04
6197 Travis Hill 1 1 6 0 0.04
6198 Valerij Bonbach 1 1 6 0 0.04
6199 Valerio Carullo 1 1 6 0 0.04
6200 Vinnie Cole 1 1 6 0 0.04
6201 Walter Sexton 1 1 6 0 0.04
6202 Wayne Wilson 1 1 6 0 0.04
6203 William Sullivan 1 1 6 0 0.04
6204 Xiuying Gong 1 1 6 0 0.04
6205 Xiuying He 1 1 6 0 0.04
6206 Yang Hu 1 1 6 0 0.04
6207 Yang Jia 1 1 6 0 0.04
6208 Yuvraj Dar 1 1 6 0 0.04
6209 Élise François-Berthelot 1 1 6 0 0.04
6210 Andrew Juarez 1 1 2 1 0.04
6211 Brass Horizon Ensemble 1 1 2 1 0.04
6212 Brendan Hawkins 1 1 2 1 0.04
6213 Brittany Simon 1 1 2 1 0.04
6214 Bruno Majtczak 1 1 2 1 0.04
6215 Cezary Siedlarz 1 1 2 1 0.04
6216 Claus-Dieter Werner-Haering 1 1 2 1 0.04
6217 Elizabeth Flores 1 1 2 1 0.04
6218 Elizabeth Rivera 1 1 2 1 0.04
6219 Halil Neuschäfer 1 1 2 1 0.04
6220 Hannes Cichorius-Seifert 1 1 2 1 0.04
6221 Hélène Lecomte-Techer 1 1 2 1 0.04
6222 Jason Mendoza 1 1 2 1 0.04
6223 Jennifer Henderson 1 1 2 1 0.04
6224 Jeremy Bryant 1 1 2 1 0.04
6225 Josephine Kabus 1 1 2 1 0.04
6226 Kendra Spears 1 1 2 1 0.04
6227 Lisa Hines 1 1 2 1 0.04
6228 Matthew Price 1 1 2 1 0.04
6229 Michael Moreno 1 1 2 1 0.04
6230 Michael Perkins 1 1 2 1 0.04
6231 Midnight Terrain 1 1 2 1 0.04
6232 Min Song 1 1 2 1 0.04
6233 Neelofar Balan 1 1 2 1 0.04
6234 Robert Carlson 1 1 2 1 0.04
6235 Ronald Warner 1 1 2 1 0.04
6236 Samiha Aurora 1 1 2 1 0.04
6237 Susan Garcia 1 1 2 1 0.04
6238 Violetta Peukert 1 1 2 1 0.04
6239 Wei Hao 1 1 2 1 0.04
6240 Aarav Balan 1 1 5 0 0.04
6241 Adam Reid 1 1 5 0 0.04
6242 Advik Shroff 1 1 5 0 0.04
6243 Alexis Vincent 1 1 5 0 0.04
6244 Alicia Rice 1 1 5 0 0.04
6245 Allison Barnes 1 1 5 0 0.04
6246 Amanda Black 1 1 5 0 0.04
6247 Amanda English 1 1 5 0 0.04
6248 Amanda Moore 1 1 5 0 0.04
6249 Amber Smith 1 1 5 0 0.04
6250 Amy Rodriguez 1 1 5 0 0.04
6251 Ana Collins 1 1 5 0 0.04
6252 Andrea Duncan 1 1 5 0 0.04
6253 Andrew Montes 1 1 5 0 0.04
6254 Angela Black 1 1 5 0 0.04
6255 Angela Rios 1 1 5 0 0.04
6256 Anita Szałata 1 1 5 0 0.04
6257 Annelise Heidrich 1 1 5 0 0.04
6258 Anthony Adams 1 1 5 0 0.04
6259 Anthony Bailey 1 1 5 0 0.04
6260 Anthony Wheeler 1 1 5 0 0.04
6261 Arnav Saran 1 1 5 0 0.04
6262 Aroa Coca 1 1 5 0 0.04
6263 Arthur Lewis 1 1 5 0 0.04
6264 Ashley Hernandez 1 1 5 0 0.04
6265 Ashley Moore 1 1 5 0 0.04
6266 Auguste Perrin 1 1 5 0 0.04
6267 Bekir Ladeck 1 1 5 0 0.04
6268 Ben Ponci 1 1 5 0 0.04
6269 Benjamin Leon 1 1 5 0 0.04
6270 Beverly Johnson 1 1 5 0 0.04
6271 Bianca Ward 1 1 5 0 0.04
6272 Bradley Small 1 1 5 0 0.04
6273 Brandi Price 1 1 5 0 0.04
6274 Brian Le 1 1 5 0 0.04
6275 Briana Johnson 1 1 5 0 0.04
6276 Brianna Williams 1 1 5 0 0.04
6277 Brittany Wright 1 1 5 0 0.04
6278 Camillo Liguori-Pistoletto 1 1 5 0 0.04
6279 Carl Serrano 1 1 5 0 0.04
6280 Carlos Anderson 1 1 5 0 0.04
6281 Carlos Davis 1 1 5 0 0.04
6282 Carrie Webb 1 1 5 0 0.04
6283 Cassandra Hernandez 1 1 5 0 0.04
6284 Cathrin Bruder 1 1 5 0 0.04
6285 Chao Kang 1 1 5 0 0.04
6286 Chao Xia 1 1 5 0 0.04
6287 Charles Horton 1 1 5 0 0.04
6288 Christine Anderson 1 1 5 0 0.04
6289 Christopher Clark 1 1 5 0 0.04
6290 Christopher Fox 1 1 5 0 0.04
6291 Christopher Richards 1 1 5 0 0.04
6292 Connie Turner 1 1 5 0 0.04
6293 Corrado Chinnici-Ferrabosco 1 1 5 0 0.04
6294 Cory Carr 1 1 5 0 0.04
6295 Cynthia Lewis 1 1 5 0 0.04
6296 Cynthia Nash 1 1 5 0 0.04
6297 Daniel Campbell 1 1 5 0 0.04
6298 Daria Ceravolo 1 1 5 0 0.04
6299 Darko Kambs 1 1 5 0 0.04
6300 Dave Miller 1 1 5 0 0.04
6301 David Hebert 1 1 5 0 0.04
6302 Denise Moody 1 1 5 0 0.04
6303 Denise Price 1 1 5 0 0.04
6304 Derek Baxter 1 1 5 0 0.04
6305 Dharmajan Lad 1 1 5 0 0.04
6306 Donna Christensen 1 1 5 0 0.04
6307 Douglas Nguyen 1 1 5 0 0.04
6308 Elizabeth Lin 1 1 5 0 0.04
6309 Emily Merritt 1 1 5 0 0.04
6310 Emma Venditti-Brugnaro 1 1 5 0 0.04
6311 Eric Sanchez 1 1 5 0 0.04
6312 Erica Poole 1 1 5 0 0.04
6313 Eva Ray 1 1 5 0 0.04
6314 Evan Solis 1 1 5 0 0.04
6315 Evangelista Muti 1 1 5 0 0.04
6316 Fang Peng 1 1 5 0 0.04
6317 Fito Pacheco León 1 1 5 0 0.04
6318 Fractured Mirrors 1 1 5 0 0.04
6319 Frau Evelyn Schmiedt 1 1 5 0 0.04
6320 Fryderyk Deptuch 1 1 5 0 0.04
6321 Gary Edwards 1 1 5 0 0.04
6322 Gatik Gandhi 1 1 5 0 0.04
6323 Gatik Thaker 1 1 5 0 0.04
6324 George Russell 1 1 5 0 0.04
6325 Gerhardt Jäkel 1 1 5 0 0.04
6326 Gerold Boucsein-Oestrovsky 1 1 5 0 0.04
6327 Geronimo Morpurgo 1 1 5 0 0.04
6328 Gokul Dhaliwal 1 1 5 0 0.04
6329 Gregory Romero 1 1 5 0 0.04
6330 Hannah Wilkinson 1 1 5 0 0.04
6331 Harri Oderwald 1 1 5 0 0.04
6332 Hector Jones 1 1 5 0 0.04
6333 Heinz-Dieter Pruschke 1 1 5 0 0.04
6334 Henry Norris 1 1 5 0 0.04
6335 Ibán Salazar 1 1 5 0 0.04
6336 Indrajit Halder 1 1 5 0 0.04
6337 Isidor Hethur 1 1 5 0 0.04
6338 Ivo Cannizzaro 1 1 5 0 0.04
6339 James Perry 1 1 5 0 0.04
6340 James Rice 1 1 5 0 0.04
6341 Jamie Walker 1 1 5 0 0.04
6342 Jaroslava Hrubá 1 1 5 0 0.04
6343 Jasmin Rogner 1 1 5 0 0.04
6344 Jason Hendricks 1 1 5 0 0.04
6345 Jayan Chawla 1 1 5 0 0.04
6346 Jeffery Mitchell 1 1 5 0 0.04
6347 Jennifer Meadows 1 1 5 0 0.04
6348 Jennifer Olson 1 1 5 0 0.04
6349 Jessica Fry 1 1 5 0 0.04
6350 Jie Bai 1 1 5 0 0.04
6351 Jie Tang 1 1 5 0 0.04
6352 Jindřich Zeman 1 1 5 0 0.04
6353 Jing Xiong 1 1 5 0 0.04
6354 Jodi Morgan 1 1 5 0 0.04
6355 John Cortez 1 1 5 0 0.04
6356 John Kemp 1 1 5 0 0.04
6357 John Wheeler 1 1 5 0 0.04
6358 Joseph Cole 1 1 5 0 0.04
6359 Joseph Walton 1 1 5 0 0.04
6360 Joshua Best 1 1 5 0 0.04
6361 Jun Lu 1 1 5 0 0.04
6362 Jun Ren 1 1 5 0 0.04
6363 Jun Sun 1 1 5 0 0.04
6364 Kaira Saxena 1 1 5 0 0.04
6365 Kana Ito 1 1 5 0 0.04
6366 Karen Sanchez 1 1 5 0 0.04
6367 Karla Säuberlich-Reichmann 1 1 5 0 0.04
6368 Katelyn Chavez 1 1 5 0 0.04
6369 Katherine Dodson 1 1 5 0 0.04
6370 Katherine Flores 1 1 5 0 0.04
6371 Katherine Gutierrez 1 1 5 0 0.04
6372 Katherine Ortega 1 1 5 0 0.04
6373 Katherine Smith 1 1 5 0 0.04
6374 Kathy Castro 1 1 5 0 0.04
6375 Katie Bryant 1 1 5 0 0.04
6376 Kelli Martinez 1 1 5 0 0.04
6377 Kelly Boyd 1 1 5 0 0.04
6378 Kelly Dickson 1 1 5 0 0.04
6379 Kick van Geffen 1 1 5 0 0.04
6380 Kimberly House 1 1 5 0 0.04
6381 Krystyna Szklarek 1 1 5 0 0.04
6382 Kunigunda Losekann 1 1 5 0 0.04
6383 Lando Bulzoni 1 1 5 0 0.04
6384 Larry Patel 1 1 5 0 0.04
6385 Lauretta Vittadello 1 1 5 0 0.04
6386 Lei Ding 1 1 5 0 0.04
6387 Lei Han 1 1 5 0 0.04
6388 Lei Mo 1 1 5 0 0.04
6389 Lei Ye 1 1 5 0 0.04
6390 Leonard Schwital 1 1 5 0 0.04
6391 Li Qiao 1 1 5 0 0.04
6392 Liam de Vrome 1 1 5 0 0.04
6393 Liesbeth Köhler 1 1 5 0 0.04
6394 Lindsey Simpson 1 1 5 0 0.04
6395 Lisa Payne 1 1 5 0 0.04
6396 Lisa Pennington 1 1 5 0 0.04
6397 Liza Mahieu-Groenestein 1 1 5 0 0.04
6398 Liza Rijcken 1 1 5 0 0.04
6399 Lucia Ceci 1 1 5 0 0.04
6400 Mackenzie Compton 1 1 5 0 0.04
6401 Madhup Banerjee 1 1 5 0 0.04
6402 Magdalene Rust 1 1 5 0 0.04
6403 Mannat Chad 1 1 5 0 0.04
6404 Maria Romero 1 1 5 0 0.04
6405 Marietta Kraushaar 1 1 5 0 0.04
6406 Mark Martinez 1 1 5 0 0.04
6407 Mark Mueller 1 1 5 0 0.04
6408 Mark Nunez 1 1 5 0 0.04
6409 Mathias Tröst 1 1 5 0 0.04
6410 Mathilde Ribeiro 1 1 5 0 0.04
6411 Matthew Burton 1 1 5 0 0.04
6412 Matthew Collins 1 1 5 0 0.04
6413 Matthew Moyer 1 1 5 0 0.04
6414 Melanie Ballard 1 1 5 0 0.04
6415 Michael Martinez 1 1 5 0 0.04
6416 Michael Patton 1 1 5 0 0.04
6417 Michael Pineda 1 1 5 0 0.04
6418 Michelle Aguirre 1 1 5 0 0.04
6419 Miki Kondo 1 1 5 0 0.04
6420 Miloš Horváth 1 1 5 0 0.04
6421 Mitchell Ross 1 1 5 0 0.04
6422 Na Du 1 1 5 0 0.04
6423 Na Lei 1 1 5 0 0.04
6424 Natalie Hernandez 1 1 5 0 0.04
6425 Natalie Wood 1 1 5 0 0.04
6426 Nermin Förster 1 1 5 0 0.04
6427 Nichole Dean 1 1 5 0 0.04
6428 Nico Casalodi 1 1 5 0 0.04
6429 Nicole Thomas 1 1 5 0 0.04
6430 Ojas Ramakrishnan 1 1 5 0 0.04
6431 Orazio Curatoli 1 1 5 0 0.04
6432 Paige Mitchell 1 1 5 0 0.04
6433 Pamela Reynolds 1 1 5 0 0.04
6434 Peter Arnold 1 1 5 0 0.04
6435 Peter Hughes 1 1 5 0 0.04
6436 Piermaria Bevilacqua-Altera 1 1 5 0 0.04
6437 Pihu Sunder 1 1 5 0 0.04
6438 Ping Qian 1 1 5 0 0.04
6439 Priyansh Bahl 1 1 5 0 0.04
6440 Puccio Farnese 1 1 5 0 0.04
6441 Qiang Qiu 1 1 5 0 0.04
6442 Qiang Yi 1 1 5 0 0.04
6443 Rachel Ford 1 1 5 0 0.04
6444 Radomír Hruška 1 1 5 0 0.04
6445 Rebecca Thompson 1 1 5 0 0.04
6446 Renee Matthews 1 1 5 0 0.04
6447 Richard Cook 1 1 5 0 0.04
6448 Robert White 1 1 5 0 0.04
6449 Robert Williams 1 1 5 0 0.04
6450 Robin Miller 1 1 5 0 0.04
6451 Roger Morgan 1 1 5 0 0.04
6452 Rolf Pölitz 1 1 5 0 0.04
6453 Ronald Johnson 1 1 5 0 0.04
6454 Ronald Santos 1 1 5 0 0.04
6455 Rosmarie Losekann 1 1 5 0 0.04
6456 Roy Dominguez 1 1 5 0 0.04
6457 Ryan Conley 1 1 5 0 0.04
6458 Ryan van 't Houteveen-Luboch 1 1 5 0 0.04
6459 Sara Ramirez 1 1 5 0 0.04
6460 Sarah Brown 1 1 5 0 0.04
6461 Sarah Collins 1 1 5 0 0.04
6462 Scott Daniel 1 1 5 0 0.04
6463 Severo Gimenez Ródenas 1 1 5 0 0.04
6464 Shalv Sarin 1 1 5 0 0.04
6465 Shanaya Dave 1 1 5 0 0.04
6466 Sharon Holland 1 1 5 0 0.04
6467 Shayak Sen 1 1 5 0 0.04
6468 Sherry Flores 1 1 5 0 0.04
6469 Shray Ranganathan 1 1 5 0 0.04
6470 Sierra Gibson 1 1 5 0 0.04
6471 Silva Speer 1 1 5 0 0.04
6472 Stella Fusani 1 1 5 0 0.04
6473 Stephen Pope 1 1 5 0 0.04
6474 Sue May 1 1 5 0 0.04
6475 Susan Briggs 1 1 5 0 0.04
6476 Susan Evans 1 1 5 0 0.04
6477 Suzanne Bonilla 1 1 5 0 0.04
6478 Tao Cao 1 1 5 0 0.04
6479 Tao Liang 1 1 5 0 0.04
6480 Tao Wang 1 1 5 0 0.04
6481 Tara Basu 1 1 5 0 0.04
6482 Tarini Bora 1 1 5 0 0.04
6483 Taylor Miller 1 1 5 0 0.04
6484 Taylor Perry 1 1 5 0 0.04
6485 Thierry Fernandez Le Bègue 1 1 5 0 0.04
6486 Tiffany Kelly 1 1 5 0 0.04
6487 Tracy Warner 1 1 5 0 0.04
6488 Tracy Warren 1 1 5 0 0.04
6489 Trevor Freeman 1 1 5 0 0.04
6490 Tyler Lee 1 1 5 0 0.04
6491 Tyler Martinez 1 1 5 0 0.04
6492 Ugolino Bettin 1 1 5 0 0.04
6493 Victoire Roger-Noël 1 1 5 0 0.04
6494 Victor Scott 1 1 5 0 0.04
6495 Virginia Stuart 1 1 5 0 0.04
6496 Vlastimil Pokorný 1 1 5 0 0.04
6497 Wanda Fuller 1 1 5 0 0.04
6498 Wei Fang 1 1 5 0 0.04
6499 Wei Fu 1 1 5 0 0.04
6500 Wei Xiang 1 1 5 0 0.04
6501 Wendy Ayala 1 1 5 0 0.04
6502 Whitney Tucker 1 1 5 0 0.04
6503 Whitney Williams 1 1 5 0 0.04
6504 William Donovan 1 1 5 0 0.04
6505 Xiuying Liu 1 1 5 0 0.04
6506 Xiuying Shao 1 1 5 0 0.04
6507 Xiuying Wu 1 1 5 0 0.04
6508 Yan Huang 1 1 5 0 0.04
6509 Yang Wang 1 1 5 0 0.04
6510 Yolanda Hernandez 1 1 5 0 0.04
6511 Yong Cai 1 1 5 0 0.04
6512 Yong Peng 1 1 5 0 0.04
6513 Zachary Ellis 1 1 5 0 0.04
6514 Zaira Peano-Castelli 1 1 5 0 0.04
6515 Zaira Verga-Cannizzaro 1 1 5 0 0.04
6516 Zeeshan Manda 1 1 5 0 0.04
6517 Deborah Hayes 1 0 11 3 0.04
6518 Pihu Chowdhury 1 0 11 3 0.04
6519 Stephanie Wilson 1 0 11 3 0.04
6520 Amber Ward 1 1 1 1 0.04
6521 Anay Dey 1 1 1 1 0.04
6522 Anna Maria Michel 1 1 1 1 0.04
6523 Aurora Calarco 1 1 1 1 0.04
6524 Daniel Bluszcz 1 1 1 1 0.04
6525 Janina Worach 1 1 1 1 0.04
6526 Joshua Taylor 1 1 1 1 0.04
6527 Shannon Harvey 1 1 1 1 0.04
6528 Veronica Burns 1 1 1 1 0.04
6529 Stephanie Roberts 1 0 14 2 0.04
6530 Juno Ray 2 0 5 0 0.04
6531 Savannah Teal 2 0 5 0 0.04
6532 Aaron Carter 1 1 4 0 0.04
6533 Abigail Hawkins 1 1 4 0 0.04
6534 Abigaíl del Franch 1 1 4 0 0.04
6535 Abril Serna 1 1 4 0 0.04
6536 Adele Curatoli 1 1 4 0 0.04
6537 Adira Bhagat 1 1 4 0 0.04
6538 Adrian Blevins 1 1 4 0 0.04
6539 Adrien Gaudin 1 1 4 0 0.04
6540 Adrienne Godard 1 1 4 0 0.04
6541 Alain du Vaillant 1 1 4 0 0.04
6542 Alejandro Baker 1 1 4 0 0.04
6543 Alex Schwartz 1 1 4 0 0.04
6544 Alex Smith 1 1 4 0 0.04
6545 Alexander Curry Jr. 1 1 4 0 0.04
6546 Alexandra Montes 1 1 4 0 0.04
6547 Alicia Jordan 1 1 4 0 0.04
6548 Allison Curtis 1 1 4 0 0.04
6549 Allison Marshall 1 1 4 0 0.04
6550 Alyssa Adams 1 1 4 0 0.04
6551 Amanda Baker 1 1 4 0 0.04
6552 Amanda Coleman 1 1 4 0 0.04
6553 Amanda Hardy 1 1 4 0 0.04
6554 Amber Sanders 1 1 4 0 0.04
6555 Amy Carroll 1 1 4 0 0.04
6556 Amy Johnston 1 1 4 0 0.04
6557 Anastasios Hölzenbecher-Henschel 1 1 4 0 0.04
6558 Anay Boase 1 1 4 0 0.04
6559 Anaïs Blanchard 1 1 4 0 0.04
6560 Andrew Cruz 1 1 4 0 0.04
6561 Andrew Lee 1 1 4 0 0.04
6562 Andy Eigenwillig 1 1 4 0 0.04
6563 Aneta Mies-Fritsch 1 1 4 0 0.04
6564 Anica Baum 1 1 4 0 0.04
6565 Anthony Burns 1 1 4 0 0.04
6566 Anthony Hamilton 1 1 4 0 0.04
6567 Anthony Padilla 1 1 4 0 0.04
6568 Anthony Silva 1 1 4 0 0.04
6569 Antonia Balotelli 1 1 4 0 0.04
6570 Ashlee White 1 1 4 0 0.04
6571 Beatrix Mende 1 1 4 0 0.04
6572 Benjamin Chen 1 1 4 0 0.04
6573 Benjamin Valencia 1 1 4 0 0.04
6574 Bernardita Marisol Marqués Sosa 1 1 4 0 0.04
6575 Beverly Pham 1 1 4 0 0.04
6576 Bobby Walters 1 1 4 0 0.04
6577 Brandon Dillon 1 1 4 0 0.04
6578 Brandon Flores 1 1 4 0 0.04
6579 Brandon Marshall 1 1 4 0 0.04
6580 Brent White 1 1 4 0 0.04
6581 Brett Mcdonald 1 1 4 0 0.04
6582 Brian Garrett 1 1 4 0 0.04
6583 Brian Hall 1 1 4 0 0.04
6584 Brian Hamilton 1 1 4 0 0.04
6585 Brian Mcbride 1 1 4 0 0.04
6586 Brian Pratt 1 1 4 0 0.04
6587 Brianna Gray 1 1 4 0 0.04
6588 Brittany Oliver 1 1 4 0 0.04
6589 Brittney Rivera 1 1 4 0 0.04
6590 Brooke Robinson 1 1 4 0 0.04
6591 Bruce Young 1 1 4 0 0.04
6592 Bryan Laffray-Recers 1 1 4 0 0.04
6593 Caleb Hunter 1 1 4 0 0.04
6594 Calvin Quinn 1 1 4 0 0.04
6595 Carly Luna 1 1 4 0 0.04
6596 Carol Wallace 1 1 4 0 0.04
6597 Caroline Lewis 1 1 4 0 0.04
6598 Caroline Williams 1 1 4 0 0.04
6599 Carolyn Ramos 1 1 4 0 0.04
6600 Carrie Griffin 1 1 4 0 0.04
6601 Casey Perez 1 1 4 0 0.04
6602 Cathy Oconnor 1 1 4 0 0.04
6603 Cemal Boucsein 1 1 4 0 0.04
6604 Chad Gibson 1 1 4 0 0.04
6605 Chao Long 1 1 4 0 0.04
6606 Chao Lu 1 1 4 0 0.04
6607 Charles White 1 1 4 0 0.04
6608 Charlotte Escobar 1 1 4 0 0.04
6609 Chelsea Harris 1 1 4 0 0.04
6610 Chelsea Liu 1 1 4 0 0.04
6611 Cheryl Miller 1 1 4 0 0.04
6612 Cheryl Peterson 1 1 4 0 0.04
6613 Chris Ward 1 1 4 0 0.04
6614 Christy Hodges 1 1 4 0 0.04
6615 Cilli Trommler 1 1 4 0 0.04
6616 Cindy Moore 1 1 4 0 0.04
6617 Cindy Zamora 1 1 4 0 0.04
6618 Clara Weitzel 1 1 4 0 0.04
6619 Claudine Martin 1 1 4 0 0.04
6620 Colleen Sandoval 1 1 4 0 0.04
6621 Cynthia Moore 1 1 4 0 0.04
6622 Célina Collin de la Chauveau 1 1 4 0 0.04
6623 Dale Suarez 1 1 4 0 0.04
6624 Dana Green 1 1 4 0 0.04
6625 Daniel Kent 1 1 4 0 0.04
6626 David Castaneda 1 1 4 0 0.04
6627 David Chandler 1 1 4 0 0.04
6628 David Hughes 1 1 4 0 0.04
6629 David Knox 1 1 4 0 0.04
6630 David Morgan 1 1 4 0 0.04
6631 Dawn Hopkins 1 1 4 0 0.04
6632 Debbie Edwards 1 1 4 0 0.04
6633 Debra Avery 1 1 4 0 0.04
6634 Devin Chan 1 1 4 0 0.04
6635 Dierk Reichmann 1 1 4 0 0.04
6636 Diya Goyal 1 1 4 0 0.04
6637 Donald Nelson 1 1 4 0 0.04
6638 Donatello Schiaparelli 1 1 4 0 0.04
6639 Douglas Griffin 1 1 4 0 0.04
6640 Duane Richardson 1 1 4 0 0.04
6641 Dylan Rice 1 1 4 0 0.04
6642 Eileen Wiley 1 1 4 0 0.04
6643 Elakshi Shukla 1 1 4 0 0.04
6644 Elena Olivera Agudo 1 1 4 0 0.04
6645 Enno Reinhardt 1 1 4 0 0.04
6646 Eric Boyd 1 1 4 0 0.04
6647 Eric King 1 1 4 0 0.04
6648 Ermanno Rosiello 1 1 4 0 0.04
6649 Ernest Mills 1 1 4 0 0.04
6650 Evelia Viñas-Maldonado 1 1 4 0 0.04
6651 Femke de Koning-Hendriks 1 1 4 0 0.04
6652 Ferdinando Nordio 1 1 4 0 0.04
6653 Fernanda Lamborghini 1 1 4 0 0.04
6654 Gabriel Rosales 1 1 4 0 0.04
6655 Gang Guo 1 1 4 0 0.04
6656 Gang Lu 1 1 4 0 0.04
6657 Gang Xiao 1 1 4 0 0.04
6658 Gatik Soni 1 1 4 0 0.04
6659 Gemma Gentilini 1 1 4 0 0.04
6660 Geraldo Moreno-Bravo 1 1 4 0 0.04
6661 Gertraud Franke-Trupp 1 1 4 0 0.04
6662 Gianfrancesco Merisi 1 1 4 0 0.04
6663 Giorgia Norbiato 1 1 4 0 0.04
6664 Greco Duse-Liverotti 1 1 4 0 0.04
6665 Guy Voisin 1 1 4 0 0.04
6666 Haley Adams 1 1 4 0 0.04
6667 Heather Dalton 1 1 4 0 0.04
6668 Herr Reinald Roht 1 1 4 0 0.04
6669 Herr Wolf-Rüdiger Werner 1 1 4 0 0.04
6670 Hiran Sarna 1 1 4 0 0.04
6671 Hugo Pareto 1 1 4 0 0.04
6672 Ian Dawson 1 1 4 0 0.04
6673 Ignatz Jüttner 1 1 4 0 0.04
6674 Indranil Bedi 1 1 4 0 0.04
6675 Ippazio Lattuada 1 1 4 0 0.04
6676 Isabel Wilson 1 1 4 0 0.04
6677 Jacob Ramirez 1 1 4 0 0.04
6678 Jacob Richards 1 1 4 0 0.04
6679 Jacob Rogers 1 1 4 0 0.04
6680 Jacqueline Bowen 1 1 4 0 0.04
6681 Jake Bailey 1 1 4 0 0.04
6682 James Hart 1 1 4 0 0.04
6683 Janice Fuentes 1 1 4 0 0.04
6684 Jared Snow 1 1 4 0 0.04
6685 Jason Miller 1 1 4 0 0.04
6686 Jeffrey Butler 1 1 4 0 0.04
6687 Jenna York 1 1 4 0 0.04
6688 Jennifer Adams 1 1 4 0 0.04
6689 Jennifer Boyer 1 1 4 0 0.04
6690 Jennifer Harris 1 1 4 0 0.04
6691 Jennifer Spencer 1 1 4 0 0.04
6692 Jennifer Vang 1 1 4 0 0.04
6693 Jessica Moreno 1 1 4 0 0.04
6694 Jessica Schroeder 1 1 4 0 0.04
6695 Jessica Valentine 1 1 4 0 0.04
6696 Jie Qian 1 1 4 0 0.04
6697 Jill Gardner 1 1 4 0 0.04
6698 Jing Zheng 1 1 4 0 0.04
6699 Jo Stewart 1 1 4 0 0.04
6700 Joanne Fowler 1 1 4 0 0.04
6701 Jodi Gilbert 1 1 4 0 0.04
6702 Jonathan Lawson 1 1 4 0 0.04
6703 Jordan Lawrence 1 1 4 0 0.04
6704 Joseph Allen 1 1 4 0 0.04
6705 Joshua Watson 1 1 4 0 0.04
6706 Juan Han 1 1 4 0 0.04
6707 Judy Elliott 1 1 4 0 0.04
6708 Julia Allen 1 1 4 0 0.04
6709 Juri Textor 1 1 4 0 0.04
6710 Justin Ellison 1 1 4 0 0.04
6711 Kacper Panasewicz 1 1 4 0 0.04
6712 Kaira Dutt 1 1 4 0 0.04
6713 Karen Jackson 1 1 4 0 0.04
6714 Karen Thomas 1 1 4 0 0.04
6715 Karen Valdez 1 1 4 0 0.04
6716 Kathleen Duran 1 1 4 0 0.04
6717 Kathryn Spencer 1 1 4 0 0.04
6718 Kayla Barnes 1 1 4 0 0.04
6719 Kelly Long 1 1 4 0 0.04
6720 Kelly Lopez 1 1 4 0 0.04
6721 Kenneth Banks 1 1 4 0 0.04
6722 Kenneth Douglas 1 1 4 0 0.04
6723 Kevin Cooke 1 1 4 0 0.04
6724 Kevin Pearson 1 1 4 0 0.04
6725 Kevin Ross 1 1 4 0 0.04
6726 Kimberly Hernandez 1 1 4 0 0.04
6727 Kimberly Lopez 1 1 4 0 0.04
6728 Klaus-Günter Bender 1 1 4 0 0.04
6729 Konstantinos Wende 1 1 4 0 0.04
6730 Kristina Cowan 1 1 4 0 0.04
6731 Kristina Martinez 1 1 4 0 0.04
6732 Krystal Moody 1 1 4 0 0.04
6733 Kumiko Saito 1 1 4 0 0.04
6734 Kyle Mason 1 1 4 0 0.04
6735 Laura Badoglio 1 1 4 0 0.04
6736 Laura Davis 1 1 4 0 0.04
6737 Laurie Rodriguez 1 1 4 0 0.04
6738 Lei Chen 1 1 4 0 0.04
6739 Leon Hrabia 1 1 4 0 0.04
6740 Leoncio de Moliner 1 1 4 0 0.04
6741 Lia Grant 1 1 4 0 0.04
6742 Liliana Morellato 1 1 4 0 0.04
6743 Lina Tirabassi 1 1 4 0 0.04
6744 Lindsay Lyons 1 1 4 0 0.04
6745 Lindsey Diaz 1 1 4 0 0.04
6746 Lori Bowers 1 1 4 0 0.04
6747 Ludovica Tamborini-Paolucci 1 1 4 0 0.04
6748 Madhup Ganesh 1 1 4 0 0.04
6749 Magnus Speer 1 1 4 0 0.04
6750 Marcia Florencia Carmona Cuéllar 1 1 4 0 0.04
6751 Margaret Vargas 1 1 4 0 0.04
6752 Marie Munoz 1 1 4 0 0.04
6753 Marilyn Douglas 1 1 4 0 0.04
6754 Mary Boyd 1 1 4 0 0.04
6755 Mary King 1 1 4 0 0.04
6756 Mary Parsons 1 1 4 0 0.04
6757 Matthew Conner PhD 1 1 4 0 0.04
6758 Matthew Dominguez 1 1 4 0 0.04
6759 Matthew Howell 1 1 4 0 0.04
6760 Maurice Harrison 1 1 4 0 0.04
6761 Mees Oude Heer 1 1 4 0 0.04
6762 Melania Filangieri 1 1 4 0 0.04
6763 Mercedes Salata 1 1 4 0 0.04
6764 Michael Baker 1 1 4 0 0.04
6765 Michael Bennett 1 1 4 0 0.04
6766 Michael Benson 1 1 4 0 0.04
6767 Michael Flowers 1 1 4 0 0.04
6768 Michael Glenn 1 1 4 0 0.04
6769 Michael Gregory 1 1 4 0 0.04
6770 Michael Jefferson 1 1 4 0 0.04
6771 Michael Mann 1 1 4 0 0.04
6772 Michael Montgomery 1 1 4 0 0.04
6773 Michael Perez 1 1 4 0 0.04
6774 Michal Žák 1 1 4 0 0.04
6775 Michelle Atkins 1 1 4 0 0.04
6776 Michelle Monnier 1 1 4 0 0.04
6777 Michelle Weiss 1 1 4 0 0.04
6778 Miki Sato 1 1 4 0 0.04
6779 Miluše Dušková 1 1 4 0 0.04
6780 Min Feng 1 1 4 0 0.04
6781 Min Lai 1 1 4 0 0.04
6782 Min Luo 1 1 4 0 0.04
6783 Ming Guo 1 1 4 0 0.04
6784 Miss Jeanette Marshall 1 1 4 0 0.04
6785 Misty Reyes 1 1 4 0 0.04
6786 Mitchell Bryan 1 1 4 0 0.04
6787 Momoko Ito 1 1 4 0 0.04
6788 Monika Bochnia 1 1 4 0 0.04
6789 Myra Devan 1 1 4 0 0.04
6790 Na Wang 1 1 4 0 0.04
6791 Naděžda Pešková 1 1 4 0 0.04
6792 Nancy Wright 1 1 4 0 0.04
6793 Naoko Yamada 1 1 4 0 0.04
6794 Navya Barad 1 1 4 0 0.04
6795 Nicholas Pineda 1 1 4 0 0.04
6796 Nichole Benton 1 1 4 0 0.04
6797 Nicoletta Soffici 1 1 4 0 0.04
6798 Nina Flaiano 1 1 4 0 0.04
6799 Océane Bouchet 1 1 4 0 0.04
6800 Paloma Merisi 1 1 4 0 0.04
6801 Pamela Crane 1 1 4 0 0.04
6802 Pamela Kent 1 1 4 0 0.04
6803 Pamela Mitchell 1 1 4 0 0.04
6804 Paul Johnson 1 1 4 0 0.04
6805 Paul-Louis Benard 1 1 4 0 0.04
6806 Perry Thompson 1 1 4 0 0.04
6807 Phillip Kane 1 1 4 0 0.04
6808 Piermaria Modugno 1 1 4 0 0.04
6809 Ping Feng 1 1 4 0 0.04
6810 Ping Kang 1 1 4 0 0.04
6811 Piya Mann 1 1 4 0 0.04
6812 Qiang Cai 1 1 4 0 0.04
6813 Qiang Gong 1 1 4 0 0.04
6814 Rachel Thompson 1 1 4 0 0.04
6815 Raghav Garde 1 1 4 0 0.04
6816 Ramona Camiscione 1 1 4 0 0.04
6817 Ranbir Balay 1 1 4 0 0.04
6818 Randy Vasquez 1 1 4 0 0.04
6819 Regina Hensley 1 1 4 0 0.04
6820 Rhonda Miller 1 1 4 0 0.04
6821 Ricardo Ward 1 1 4 0 0.04
6822 Robert Harris 1 1 4 0 0.04
6823 Robert Mathis 1 1 4 0 0.04
6824 Robert Quinn 1 1 4 0 0.04
6825 Robin Harris 1 1 4 0 0.04
6826 Roksana Wlaźlak 1 1 4 0 0.04
6827 Romil Sibal 1 1 4 0 0.04
6828 Ronald Parks 1 1 4 0 0.04
6829 Ronald Yates 1 1 4 0 0.04
6830 Roy Miller 1 1 4 0 0.04
6831 Ryan Pollard 1 1 4 0 0.04
6832 Sabina Hrušková 1 1 4 0 0.04
6833 Sabine Ollivier-Morel 1 1 4 0 0.04
6834 Samantha Munoz 1 1 4 0 0.04
6835 Samantha Padilla 1 1 4 0 0.04
6836 Samar Luthra 1 1 4 0 0.04
6837 Samiha Chander 1 1 4 0 0.04
6838 Sana Batta 1 1 4 0 0.04
6839 Sana Sunder 1 1 4 0 0.04
6840 Sara Berger 1 1 4 0 0.04
6841 Sarah Clark 1 1 4 0 0.04
6842 Sarah Gray 1 1 4 0 0.04
6843 Sarah Hunter 1 1 4 0 0.04
6844 Sawyer and the Frontiers 1 1 4 0 0.04
6845 Scott Burke 1 1 4 0 0.04
6846 Scott Garcia 1 1 4 0 0.04
6847 Sean Jackson 1 1 4 0 0.04
6848 Sean Lewis 1 1 4 0 0.04
6849 Sean Mendoza 1 1 4 0 0.04
6850 Sean Phillips 1 1 4 0 0.04
6851 Sean Rogers 1 1 4 0 0.04
6852 Seve Francisco 1 1 4 0 0.04
6853 Sonia Innocenti-Gasperi 1 1 4 0 0.04
6854 Stephanie Johnson 1 1 4 0 0.04
6855 Stephen Vazquez 1 1 4 0 0.04
6856 Steven Calderon 1 1 4 0 0.04
6857 Steven Hobbs 1 1 4 0 0.04
6858 Stéphanie Parent 1 1 4 0 0.04
6859 Susan Brown 1 1 4 0 0.04
6860 Susan Spencer 1 1 4 0 0.04
6861 Susan Wong 1 1 4 0 0.04
6862 Sylwia Skowyra 1 1 4 0 0.04
6863 Taimur Iyer 1 1 4 0 0.04
6864 Tamara Douglas 1 1 4 0 0.04
6865 Tammy Camacho 1 1 4 0 0.04
6866 Tao Shi 1 1 4 0 0.04
6867 Tara Timmermans 1 1 4 0 0.04
6868 Terri Johnson 1 1 4 0 0.04
6869 Terry Nelson 1 1 4 0 0.04
6870 Thekla Schuster 1 1 4 0 0.04
6871 Theresa Jones 1 1 4 0 0.04
6872 Thilo Schmiedt 1 1 4 0 0.04
6873 Thomas Carter 1 1 4 0 0.04
6874 Thomas Palmer 1 1 4 0 0.04
6875 Tiffany Martinez 1 1 4 0 0.04
6876 Tim Slingerland 1 1 4 0 0.04
6877 Timothy Hall 1 1 4 0 0.04
6878 Timothy Padilla 1 1 4 0 0.04
6879 Timothy Robinson 1 1 4 0 0.04
6880 Todd Molina 1 1 4 0 0.04
6881 Tommy Murphy 1 1 4 0 0.04
6882 Urban Budig 1 1 4 0 0.04
6883 Urte Bachmann 1 1 4 0 0.04
6884 Venancio Belletini 1 1 4 0 0.04
6885 Veronica Koch 1 1 4 0 0.04
6886 Vicki Shepard 1 1 4 0 0.04
6887 Victor Miller 1 1 4 0 0.04
6888 Victoria Vazquez 1 1 4 0 0.04
6889 Viviana Escrivá Porcel 1 1 4 0 0.04
6890 Véronique Bernard 1 1 4 0 0.04
6891 Wanda Ray 1 1 4 0 0.04
6892 Wesley Ford 1 1 4 0 0.04
6893 William Ball 1 1 4 0 0.04
6894 William Diaz 1 1 4 0 0.04
6895 Willie Phelps 1 1 4 0 0.04
6896 Xiulan Mao 1 1 4 0 0.04
6897 Xiuying Su 1 1 4 0 0.04
6898 Yan Xiang 1 1 4 0 0.04
6899 Yan Xu 1 1 4 0 0.04
6900 Yasuhiro Yoshida 1 1 4 0 0.04
6901 Yong Xu 1 1 4 0 0.04
6902 Yves Gaudin 1 1 4 0 0.04
6903 Yvonne Morris 1 1 4 0 0.04
6904 Zachary Cole 1 1 4 0 0.04
6905 Émile Didier 1 1 4 0 0.04
6906 Éric Lamy 1 1 4 0 0.04
6907 Éric Louis-Lévy 1 1 4 0 0.04
6908 Aayush Cheema 1 1 3 0 0.03
6909 Adam Allen 1 1 3 0 0.03
6910 Adam Carter 1 1 3 0 0.03
6911 Adam Gill 1 1 3 0 0.03
6912 Adrian Jeszka 1 1 3 0 0.03
6913 Aitor Alcántara Diez 1 1 3 0 0.03
6914 Alain Delannoy 1 1 3 0 0.03
6915 Alan Bray 1 1 3 0 0.03
6916 Aleksander Grześków 1 1 3 0 0.03
6917 Aleksander Sypień 1 1 3 0 0.03
6918 Alex Bridges 1 1 3 0 0.03
6919 Alex Leblanc 1 1 3 0 0.03
6920 Alexander Lawrence 1 1 3 0 0.03
6921 Alexandra King 1 1 3 0 0.03
6922 Alexandra Mcdaniel 1 1 3 0 0.03
6923 Alexandra Scott 1 1 3 0 0.03
6924 Alexei Gotthard 1 1 3 0 0.03
6925 Alexis Moore 1 1 3 0 0.03
6926 Alisha Hartman 1 1 3 0 0.03
6927 Alix Chevallier 1 1 3 0 0.03
6928 Alma Vollbrecht 1 1 3 0 0.03
6929 Amanda Davis 1 1 3 0 0.03
6930 Amber Nguyen 1 1 3 0 0.03
6931 Amber Park 1 1 3 0 0.03
6932 Amber Ramsey 1 1 3 0 0.03
6933 Amber Rodriguez 1 1 3 0 0.03
6934 Amedeo Mimun 1 1 3 0 0.03
6935 Amy Alexander 1 1 3 0 0.03
6936 Amy Barnes 1 1 3 0 0.03
6937 Amy Bright 1 1 3 0 0.03
6938 Amy Campbell 1 1 3 0 0.03
6939 Amy Riley 1 1 3 0 0.03
6940 Amélie Peltier 1 1 3 0 0.03
6941 Andrew Garcia 1 1 3 0 0.03
6942 Andrew Nolan 1 1 3 0 0.03
6943 Angela Collins 1 1 3 0 0.03
6944 Angela Lopez 1 1 3 0 0.03
6945 Angela Perez 1 1 3 0 0.03
6946 Animated Antics 1 1 3 0 0.03
6947 Anna Fechner 1 1 3 0 0.03
6948 Anne Frederick 1 1 3 0 0.03
6949 Anne Verwoert-Slagmolen 1 1 3 0 0.03
6950 Annetta Molesini 1 1 3 0 0.03
6951 Anouk Carre 1 1 3 0 0.03
6952 Anthony Peters 1 1 3 0 0.03
6953 Antonia Steinberg-Löffler 1 1 3 0 0.03
6954 Antonio Morse 1 1 3 0 0.03
6955 Antonio Schroeder 1 1 3 0 0.03
6956 April Mills 1 1 3 0 0.03
6957 April Nelson 1 1 3 0 0.03
6958 Ashley Huang 1 1 3 0 0.03
6959 Ashley Mahoney 1 1 3 0 0.03
6960 Ashley Olsen 1 1 3 0 0.03
6961 Audrey Frye 1 1 3 0 0.03
6962 Auguste de la Laroche 1 1 3 0 0.03
6963 Augusto Franzese 1 1 3 0 0.03
6964 Aurora Vendetti 1 1 3 0 0.03
6965 Aysel Scheibe 1 1 3 0 0.03
6966 Beatrice Albers 1 1 3 0 0.03
6967 Benjamin Dupuis-Lévy 1 1 3 0 0.03
6968 Bernard-Alain Parent 1 1 3 0 0.03
6969 Bernardo Josep Mayol Manjón 1 1 3 0 0.03
6970 Bonnie Roberts 1 1 3 0 0.03
6971 Bradley Anderson 1 1 3 0 0.03
6972 Bradley Gray 1 1 3 0 0.03
6973 Brandi Sutton 1 1 3 0 0.03
6974 Brandon Banks 1 1 3 0 0.03
6975 Brenda Boyd 1 1 3 0 0.03
6976 Brett Terry 1 1 3 0 0.03
6977 Brian Olsen 1 1 3 0 0.03
6978 Brian Oneal 1 1 3 0 0.03
6979 Brian Vincent 1 1 3 0 0.03
6980 Briana Harris 1 1 3 0 0.03
6981 Brianna Hopkins 1 1 3 0 0.03
6982 Brigitte Langlois 1 1 3 0 0.03
6983 Carl Benjamin 1 1 3 0 0.03
6984 Carla Holden 1 1 3 0 0.03
6985 Carolyn Morgan 1 1 3 0 0.03
6986 Catherine Perez 1 1 3 0 0.03
6987 Catherine Roberts 1 1 3 0 0.03
6988 Cato Meyer 1 1 3 0 0.03
6989 Cesar Nixon 1 1 3 0 0.03
6990 Chao Yin 1 1 3 0 0.03
6991 Chelsea Blake 1 1 3 0 0.03
6992 Chirag Bahri 1 1 3 0 0.03
6993 Chris Cook 1 1 3 0 0.03
6994 Christian Gray 1 1 3 0 0.03
6995 Christina Mckay 1 1 3 0 0.03
6996 Christine Brown 1 1 3 0 0.03
6997 Christine Johnson 1 1 3 0 0.03
6998 Christopher Reilly 1 1 3 0 0.03
6999 Christopher Wood 1 1 3 0 0.03
7000 Christopher Young 1 1 3 0 0.03
7001 Chuy Calderon Chaves 1 1 3 0 0.03
7002 Cindy Hernandez 1 1 3 0 0.03
7003 Cindy Parker 1 1 3 0 0.03
7004 Colton Williams 1 1 3 0 0.03
7005 Cory Garrett 1 1 3 0 0.03
7006 Courtney Johnston 1 1 3 0 0.03
7007 Cynthia Jones 1 1 3 0 0.03
7008 Damiano Gelli-Verga 1 1 3 0 0.03
7009 Dana Baker 1 1 3 0 0.03
7010 Dana Medina 1 1 3 0 0.03
7011 Dana Perkins 1 1 3 0 0.03
7012 Daniel Oneill 1 1 3 0 0.03
7013 Daniel Williams 1 1 3 0 0.03
7014 Danny Lindsey 1 1 3 0 0.03
7015 Darshit Mannan 1 1 3 0 0.03
7016 David Choi 1 1 3 0 0.03
7017 David Cruz 1 1 3 0 0.03
7018 David Maddox 1 1 3 0 0.03
7019 David Orr 1 1 3 0 0.03
7020 David Richardson 1 1 3 0 0.03
7021 Dawn Massey 1 1 3 0 0.03
7022 Dawn Mcguire 1 1 3 0 0.03
7023 Deborah Aguilar 1 1 3 0 0.03
7024 Deborah Martinez 1 1 3 0 0.03
7025 Deborah Norton 1 1 3 0 0.03
7026 Deborah Osborne 1 1 3 0 0.03
7027 Debra Sims 1 1 3 0 0.03
7028 Denise Perez 1 1 3 0 0.03
7029 Derek Robinson 1 1 3 0 0.03
7030 Destiny Griffin 1 1 3 0 0.03
7031 Devon White 1 1 3 0 0.03
7032 Dhanush Baria 1 1 3 0 0.03
7033 Diane Anderson 1 1 3 0 0.03
7034 Divij Dora 1 1 3 0 0.03
7035 Dominique Delacruz 1 1 3 0 0.03
7036 Donna Nelson 1 1 3 0 0.03
7037 Doris Hatfield 1 1 3 0 0.03
7038 Eckard Werner 1 1 3 0 0.03
7039 Edward Little 1 1 3 0 0.03
7040 Edward Randolph 1 1 3 0 0.03
7041 Elizabeth Walters 1 1 3 0 0.03
7042 Emily Huynh 1 1 3 0 0.03
7043 Eric Armstrong 1 1 3 0 0.03
7044 Erica Chandler 1 1 3 0 0.03
7045 Erica Herrera 1 1 3 0 0.03
7046 Erik Huang Jr. 1 1 3 0 0.03
7047 Erin Mitchell 1 1 3 0 0.03
7048 Ernest Clark 1 1 3 0 0.03
7049 Ethan Moore 1 1 3 0 0.03
7050 Fang Jin 1 1 3 0 0.03
7051 Fang Wang 1 1 3 0 0.03
7052 Fanny Dörschner 1 1 3 0 0.03
7053 Ferenc Mälzer 1 1 3 0 0.03
7054 Fidel Gras Aller 1 1 3 0 0.03
7055 Flavio del Galán 1 1 3 0 0.03
7056 Flora Casaleggio-Endrizzi 1 1 3 0 0.03
7057 Frederick Jackson Jr. 1 1 3 0 0.03
7058 Fredo Piccinni 1 1 3 0 0.03
7059 Frieda Mitschke 1 1 3 0 0.03
7060 Gang Feng 1 1 3 0 0.03
7061 George Gutierrez 1 1 3 0 0.03
7062 Gerald Collins 1 1 3 0 0.03
7063 Giesela Kuhl 1 1 3 0 0.03
7064 Gina Miller 1 1 3 0 0.03
7065 Gionata Toldo 1 1 3 0 0.03
7066 Greca Canali 1 1 3 0 0.03
7067 Gregory Hansen 1 1 3 0 0.03
7068 Guillaume Cohen du Leduc 1 1 3 0 0.03
7069 Guillaume Petitjean 1 1 3 0 0.03
7070 Guiying Wei 1 1 3 0 0.03
7071 Guiying Zhu 1 1 3 0 0.03
7072 Hannah Hopkins 1 1 3 0 0.03
7073 Heather Kennedy 1 1 3 0 0.03
7074 Heather Velasquez 1 1 3 0 0.03
7075 Heather Wallace 1 1 3 0 0.03
7076 Henrik Seifert 1 1 3 0 0.03
7077 Ida Vigliotti 1 1 3 0 0.03
7078 Ignatz Thanel-Hornich 1 1 3 0 0.03
7079 Ildiko Wesack 1 1 3 0 0.03
7080 Isa Carmichael 1 1 3 0 0.03
7081 Isabella Turner 1 1 3 0 0.03
7082 Isabelle Godfrey van Alemannië 1 1 3 0 0.03
7083 Ise Scardino 1 1 3 0 0.03
7084 Ivo Ziegert 1 1 3 0 0.03
7085 Jackie Johnston 1 1 3 0 0.03
7086 Jacob Arnold 1 1 3 0 0.03
7087 Jacob Gomez 1 1 3 0 0.03
7088 Jacob Romero 1 1 3 0 0.03
7089 Jacqueline Miller 1 1 3 0 0.03
7090 Jacques Imbert de la Munoz 1 1 3 0 0.03
7091 James Calderon 1 1 3 0 0.03
7092 James Middleton 1 1 3 0 0.03
7093 James Simon 1 1 3 0 0.03
7094 Jamie Smith 1 1 3 0 0.03
7095 Jason Chavez 1 1 3 0 0.03
7096 Jason Jones 1 1 3 0 0.03
7097 Jason Mckenzie 1 1 3 0 0.03
7098 Jeanette Bass 1 1 3 0 0.03
7099 Jeff Farmer 1 1 3 0 0.03
7100 Jeffrey Knight 1 1 3 0 0.03
7101 Jennifer Allen 1 1 3 0 0.03
7102 Jennifer Garcia 1 1 3 0 0.03
7103 Jeremy Griffith 1 1 3 0 0.03
7104 Jeremy Jones 1 1 3 0 0.03
7105 Jeremy Russell 1 1 3 0 0.03
7106 Jermaine Gibbs 1 1 3 0 0.03
7107 Jerry Abbott 1 1 3 0 0.03
7108 Jessica Ward 1 1 3 0 0.03
7109 Jessica Wyatt 1 1 3 0 0.03
7110 Jesus Hatfield 1 1 3 0 0.03
7111 Jie Gong 1 1 3 0 0.03
7112 Jie Liao 1 1 3 0 0.03
7113 Jie Luo 1 1 3 0 0.03
7114 Jimmy Arnold 1 1 3 0 0.03
7115 Jing Hao 1 1 3 0 0.03
7116 Jing Peng 1 1 3 0 0.03
7117 Jing Tao 1 1 3 0 0.03
7118 Jivika Keer 1 1 3 0 0.03
7119 Jiya Raman 1 1 3 0 0.03
7120 Jody Jones 1 1 3 0 0.03
7121 Joel Foster 1 1 3 0 0.03
7122 Joerg Schleich 1 1 3 0 0.03
7123 Johannes Linke 1 1 3 0 0.03
7124 John Harris 1 1 3 0 0.03
7125 John May 1 1 3 0 0.03
7126 John Mcdonald 1 1 3 0 0.03
7127 John Rivera 1 1 3 0 0.03
7128 John Taylor 1 1 3 0 0.03
7129 John Watts 1 1 3 0 0.03
7130 John Wright 1 1 3 0 0.03
7131 Jonathan Barker 1 1 3 0 0.03
7132 Jonathan Oestrovsky 1 1 3 0 0.03
7133 Jordan Lutz 1 1 3 0 0.03
7134 Jose Cerdán Paniagua 1 1 3 0 0.03
7135 Jose Manuel Quintana Borrell 1 1 3 0 0.03
7136 Jose Martin 1 1 3 0 0.03
7137 Joseph Dillon 1 1 3 0 0.03
7138 Joseph Jones 1 1 3 0 0.03
7139 Joseph Moore 1 1 3 0 0.03
7140 Joseph Roman 1 1 3 0 0.03
7141 Joseph Stevens 1 1 3 0 0.03
7142 Joseph Thomas 1 1 3 0 0.03
7143 Joseph Villa 1 1 3 0 0.03
7144 Joshua Brewer 1 1 3 0 0.03
7145 Joshua Harris 1 1 3 0 0.03
7146 Joséphine Rodrigues 1 1 3 0 0.03
7147 Juan Shelton 1 1 3 0 0.03
7148 Julie Taylor 1 1 3 0 0.03
7149 Julita Kudełka 1 1 3 0 0.03
7150 Jun Zhao 1 1 3 0 0.03
7151 Justin Morales 1 1 3 0 0.03
7152 Kamil Bílek 1 1 3 0 0.03
7153 Kamil Sura 1 1 3 0 0.03
7154 Karl-Josef Reinhardt 1 1 3 0 0.03
7155 Kathleen Valentine 1 1 3 0 0.03
7156 Kathy Green 1 1 3 0 0.03
7157 Katie Fleming 1 1 3 0 0.03
7158 Katie Richards 1 1 3 0 0.03
7159 Kayla Jenkins 1 1 3 0 0.03
7160 Kayla Roberson 1 1 3 0 0.03
7161 Kazuya Kobayashi 1 1 3 0 0.03
7162 Kazuya Maeda 1 1 3 0 0.03
7163 Kazuya Matsumoto 1 1 3 0 0.03
7164 Kelli Mckinney 1 1 3 0 0.03
7165 Kelly Marshall 1 1 3 0 0.03
7166 Kelly Smith 1 1 3 0 0.03
7167 Kenneth Jimenez 1 1 3 0 0.03
7168 Kenneth Robinson 1 1 3 0 0.03
7169 Kenneth Ross 1 1 3 0 0.03
7170 Kevin Richardson 1 1 3 0 0.03
7171 Kimberly Campbell 1 1 3 0 0.03
7172 Kimberly Guerrero 1 1 3 0 0.03
7173 Kimberly Murphy 1 1 3 0 0.03
7174 Kimberly Thomas 1 1 3 0 0.03
7175 Kirk Kemp 1 1 3 0 0.03
7176 Kreszentia Staude 1 1 3 0 0.03
7177 Krista Collins 1 1 3 0 0.03
7178 Kristen Stanley 1 1 3 0 0.03
7179 Kristina Boyer 1 1 3 0 0.03
7180 Kristy Miller 1 1 3 0 0.03
7181 Kristy Olsen 1 1 3 0 0.03
7182 Laila Rosenow-Mans 1 1 3 0 0.03
7183 Lance Garcia 1 1 3 0 0.03
7184 Lando Parri 1 1 3 0 0.03
7185 Larry Miller 1 1 3 0 0.03
7186 Latasha Watts 1 1 3 0 0.03
7187 Lawrence Guzman 1 1 3 0 0.03
7188 Leah Moore 1 1 3 0 0.03
7189 Lei Hou 1 1 3 0 0.03
7190 Lei Meng 1 1 3 0 0.03
7191 Lei Ren 1 1 3 0 0.03
7192 Lei Song 1 1 3 0 0.03
7193 Leonard Suarez 1 1 3 0 0.03
7194 Li Meng 1 1 3 0 0.03
7195 Lindsay Ponce 1 1 3 0 0.03
7196 Lisa Baker 1 1 3 0 0.03
7197 Lisa Fuentes 1 1 3 0 0.03
7198 Lisa Hanna 1 1 3 0 0.03
7199 Lisa Kirk 1 1 3 0 0.03
7200 Lisa Mccoy 1 1 3 0 0.03
7201 Liwia Cygal 1 1 3 0 0.03
7202 Louis Herman 1 1 3 0 0.03
7203 Luc Hoarau 1 1 3 0 0.03
7204 Luca Gori-Visconti 1 1 3 0 0.03
7205 Luciano Calzada Arcos 1 1 3 0 0.03
7206 Luigina Fibonacci-Caruso 1 1 3 0 0.03
7207 Luisa Tommaseo 1 1 3 0 0.03
7208 Luke Payne 1 1 3 0 0.03
7209 Lupe Neira 1 1 3 0 0.03
7210 Mackenzie Murray 1 1 3 0 0.03
7211 Madeleine Raymond 1 1 3 0 0.03
7212 Madison Rodriguez 1 1 3 0 0.03
7213 Mahika Basu 1 1 3 0 0.03
7214 Manikya Toor 1 1 3 0 0.03
7215 Manjari Bobal 1 1 3 0 0.03
7216 Mannat Edwin 1 1 3 0 0.03
7217 Marcella Belletini 1 1 3 0 0.03
7218 Margherita Giammusso 1 1 3 0 0.03
7219 Margot Becker-Striebitz 1 1 3 0 0.03
7220 Maria Matthews 1 1 3 0 0.03
7221 Marie-Louise Wesack 1 1 3 0 0.03
7222 Marina Trapani 1 1 3 0 0.03
7223 Mariola Heser 1 1 3 0 0.03
7224 Martin Guilbert 1 1 3 0 0.03
7225 Marvin Heuser 1 1 3 0 0.03
7226 Mary Johnson 1 1 3 0 0.03
7227 Mary Walker 1 1 3 0 0.03
7228 Mary White 1 1 3 0 0.03
7229 Matthew Mitchell 1 1 3 0 0.03
7230 Matthäus Hendriks 1 1 3 0 0.03
7231 Megan Marsh 1 1 3 0 0.03
7232 Meike Döhn 1 1 3 0 0.03
7233 Melissa Watkins 1 1 3 0 0.03
7234 Mercedes Torlonia 1 1 3 0 0.03
7235 Michael Barry 1 1 3 0 0.03
7236 Michael Hayes 1 1 3 0 0.03
7237 Michael Johnson 1 1 3 0 0.03
7238 Michael Martin 1 1 3 0 0.03
7239 Michael Mccullough 1 1 3 0 0.03
7240 Michael Tapia 1 1 3 0 0.03
7241 Michael Watson 1 1 3 0 0.03
7242 Micheal Welch 1 1 3 0 0.03
7243 Michele Kim 1 1 3 0 0.03
7244 Michelle Gonzalez 1 1 3 0 0.03
7245 Michelle Johnson 1 1 3 0 0.03
7246 Miki Watanabe 1 1 3 0 0.03
7247 Milada Svobodová 1 1 3 0 0.03
7248 Milo Hendriks 1 1 3 0 0.03
7249 Miranda Robinson 1 1 3 0 0.03
7250 Miss Cynthia Myers 1 1 3 0 0.03
7251 Mitchell Sharp 1 1 3 0 0.03
7252 Mojmír Hruška 1 1 3 0 0.03
7253 Monica Blevins 1 1 3 0 0.03
7254 Monica Murphy 1 1 3 0 0.03
7255 Monica Ramirez 1 1 3 0 0.03
7256 Morgan Carlson 1 1 3 0 0.03
7257 Morgan Shelton 1 1 3 0 0.03
7258 Moritz Keudel 1 1 3 0 0.03
7259 Nakul Gole 1 1 3 0 0.03
7260 Naoto Suzuki 1 1 3 0 0.03
7261 Nath Henry 1 1 3 0 0.03
7262 Nathan Nelson 1 1 3 0 0.03
7263 Nayantara Dugal 1 1 3 0 0.03
7264 Nicholas Gonzalez 1 1 3 0 0.03
7265 Nicholas Mckinney 1 1 3 0 0.03
7266 Nicholas Morgan 1 1 3 0 0.03
7267 Nino Veneziano 1 1 3 0 0.03
7268 Nirvaan Thaker 1 1 3 0 0.03
7269 Odette Dupuy 1 1 3 0 0.03
7270 Omnira 1 1 3 0 0.03
7271 Osamu Yamashita 1 1 3 0 0.03
7272 Oto Macháček 1 1 3 0 0.03
7273 Paloma Fogazzaro 1 1 3 0 0.03
7274 Pamela Ayala 1 1 3 0 0.03
7275 Pamela Elliott 1 1 3 0 0.03
7276 Pamela Massey 1 1 3 0 0.03
7277 Parinaaz Deshmukh 1 1 3 0 0.03
7278 Pascal Pärtzelt-Jockel 1 1 3 0 0.03
7279 Patrick Williams 1 1 3 0 0.03
7280 Paweł Gęca 1 1 3 0 0.03
7281 Peter Knapp 1 1 3 0 0.03
7282 Philip George 1 1 3 0 0.03
7283 Pia Schleich 1 1 3 0 0.03
7284 Pina Garrone 1 1 3 0 0.03
7285 Ping Yang 1 1 3 0 0.03
7286 Rachel Garcia 1 1 3 0 0.03
7287 Rafael Hoz Pizarro 1 1 3 0 0.03
7288 Ralph Bell 1 1 3 0 0.03
7289 Rania Chadha 1 1 3 0 0.03
7290 Raunak Rana 1 1 3 0 0.03
7291 Raymond Hornich-Jacob 1 1 3 0 0.03
7292 Raymond Robinson 1 1 3 0 0.03
7293 Rebecca Schultz 1 1 3 0 0.03
7294 Regina Beck 1 1 3 0 0.03
7295 Rei Sato 1 1 3 0 0.03
7296 Renee Walker 1 1 3 0 0.03
7297 Renske Godfrey van Alemannië 1 1 3 0 0.03
7298 Renée Bègue-Giraud 1 1 3 0 0.03
7299 Rhythm Collective 1 1 3 0 0.03
7300 Richard Francis 1 1 3 0 0.03
7301 Richard Moore 1 1 3 0 0.03
7302 Ricky Richards 1 1 3 0 0.03
7303 Robert Chen 1 1 3 0 0.03
7304 Robert Jefferson 1 1 3 0 0.03
7305 Robert Ryan 1 1 3 0 0.03
7306 Robin Cline 1 1 3 0 0.03
7307 Robin Mayer 1 1 3 0 0.03
7308 Rodolfo Montecchi-Bonomo 1 1 3 0 0.03
7309 Roman Doria 1 1 3 0 0.03
7310 Rosalinde Kreusel-Mühle 1 1 3 0 0.03
7311 Roy Tran 1 1 3 0 0.03
7312 Ruben Watkins 1 1 3 0 0.03
7313 Ryan Morales 1 1 3 0 0.03
7314 Ryan Osborn 1 1 3 0 0.03
7315 Rémy Jourdan 1 1 3 0 0.03
7316 Sabrina Marquez 1 1 3 0 0.03
7317 Saksham Borra 1 1 3 0 0.03
7318 Sandra Miller 1 1 3 0 0.03
7319 Sara Laszuk 1 1 3 0 0.03
7320 Sarah Caldwell 1 1 3 0 0.03
7321 Sarah Riley 1 1 3 0 0.03
7322 Sarah Sims 1 1 3 0 0.03
7323 Savannah Leblanc 1 1 3 0 0.03
7324 Scott Nichols 1 1 3 0 0.03
7325 Scott Schwartz 1 1 3 0 0.03
7326 Selena Gonzalez 1 1 3 0 0.03
7327 Shane Alexander 1 1 3 0 0.03
7328 Shannon Jacobson 1 1 3 0 0.03
7329 Sharon Mitchell 1 1 3 0 0.03
7330 Sheena Lane 1 1 3 0 0.03
7331 Sheila Flores 1 1 3 0 0.03
7332 Sherry Bell 1 1 3 0 0.03
7333 Sherry Mcdaniel 1 1 3 0 0.03
7334 Stacy Moore 1 1 3 0 0.03
7335 Stanislav Prokop 1 1 3 0 0.03
7336 Stephanie Fowler 1 1 3 0 0.03
7337 Stephanie Gray 1 1 3 0 0.03
7338 Stephen Santiago 1 1 3 0 0.03
7339 Steve Bailey 1 1 3 0 0.03
7340 Steven Berg 1 1 3 0 0.03
7341 Steven Weaver 1 1 3 0 0.03
7342 Summer Mahoney 1 1 3 0 0.03
7343 Susan Francis 1 1 3 0 0.03
7344 Susan Randall 1 1 3 0 0.03
7345 Susan Rodriguez 1 1 3 0 0.03
7346 Susanna Raimondi 1 1 3 0 0.03
7347 Suzanne Martinez 1 1 3 0 0.03
7348 Tammy Campos 1 1 3 0 0.03
7349 Tammy Howell 1 1 3 0 0.03
7350 Tao Zhong 1 1 3 0 0.03
7351 Taro Mori 1 1 3 0 0.03
7352 Taro Sakamoto 1 1 3 0 0.03
7353 Tatjana Gieß 1 1 3 0 0.03
7354 Taylor Hayes 1 1 3 0 0.03
7355 Taylor Martin 1 1 3 0 0.03
7356 Teresa Nelson 1 1 3 0 0.03
7357 Terry Adams 1 1 3 0 0.03
7358 Theodore Simmons 1 1 3 0 0.03
7359 Theresa Johnson 1 1 3 0 0.03
7360 Thomas Meyer 1 1 3 0 0.03
7361 Tim Morgan 1 1 3 0 0.03
7362 Timothy Sanders 1 1 3 0 0.03
7363 Timothée Charles 1 1 3 0 0.03
7364 Tina Hart 1 1 3 0 0.03
7365 Tina Morgan 1 1 3 0 0.03
7366 Tina Spencer 1 1 3 0 0.03
7367 Todd Thompson 1 1 3 0 0.03
7368 Tonya May 1 1 3 0 0.03
7369 Tonya Wright 1 1 3 0 0.03
7370 Travis Price 1 1 3 0 0.03
7371 Trevor Singh 1 1 3 0 0.03
7372 Tyler Case 1 1 3 0 0.03
7373 Tyler Clay 1 1 3 0 0.03
7374 Ulrich Klotz 1 1 3 0 0.03
7375 Valerie Pena 1 1 3 0 0.03
7376 Valérie Valette 1 1 3 0 0.03
7377 Vicenta Castañeda Arroyo 1 1 3 0 0.03
7378 Victoria Velazquez 1 1 3 0 0.03
7379 Wei Ren 1 1 3 0 0.03
7380 Wei Yan 1 1 3 0 0.03
7381 Wendy Nelson 1 1 3 0 0.03
7382 Whitney Schmidt 1 1 3 0 0.03
7383 William Delacruz 1 1 3 0 0.03
7384 William Francis 1 1 3 0 0.03
7385 William Shields 1 1 3 0 0.03
7386 Wolfgang Bärer-Holt 1 1 3 0 0.03
7387 Xavier Baron 1 1 3 0 0.03
7388 Xiuying Feng 1 1 3 0 0.03
7389 Xiuying Zhao 1 1 3 0 0.03
7390 Yan Jiang 1 1 3 0 0.03
7391 Yan Lai 1 1 3 0 0.03
7392 Yan Yi 1 1 3 0 0.03
7393 Yang Qian 1 1 3 0 0.03
7394 Yang Xia 1 1 3 0 0.03
7395 Yashvi Bahri 1 1 3 0 0.03
7396 Yolanda Lee 1 1 3 0 0.03
7397 Yong Jiang 1 1 3 0 0.03
7398 Yong Xiao 1 1 3 0 0.03
7399 Yvette Cohen 1 1 3 0 0.03
7400 Yvonne Mullins 1 1 3 0 0.03
7401 Zachary Wells 1 1 3 0 0.03
7402 Zara Kibe 1 1 3 0 0.03
7403 Zbigniew Stey 1 1 3 0 0.03
7404 Frau Viola Ullmann 1 0 12 2 0.03
7405 Ivona Žáková 1 0 12 2 0.03
7406 Thies Seifert 1 0 12 2 0.03
7407 Cole Pierce 2 0 3 0 0.03
7408 Holden Rhys 2 0 3 0 0.03
7409 Max Everhart 2 0 3 0 0.03
7410 Abigail Scott 1 1 2 0 0.03
7411 Abram Kala 1 1 2 0 0.03
7412 Adam Fletcher 1 1 2 0 0.03
7413 Adam Kroker 1 1 2 0 0.03
7414 Adrianna Panasiewicz 1 1 2 0 0.03
7415 Advika Tailor 1 1 2 0 0.03
7416 Adélaïde Letellier de Cordier 1 1 2 0 0.03
7417 Agata Bähr-Metz 1 1 2 0 0.03
7418 Ahana Babu 1 1 2 0 0.03
7419 Alessandro Zito 1 1 2 0 0.03
7420 Alexa Adolph 1 1 2 0 0.03
7421 Alexis Hart 1 1 2 0 0.03
7422 Alf Bähr 1 1 2 0 0.03
7423 Alisha Fernandez 1 1 2 0 0.03
7424 Allison Williams 1 1 2 0 0.03
7425 Amanda Cole 1 1 2 0 0.03
7426 Anahi Buch 1 1 2 0 0.03
7427 Andrea White 1 1 2 0 0.03
7428 Andree Dippel 1 1 2 0 0.03
7429 Andree Tschentscher 1 1 2 0 0.03
7430 Andrew Carrillo 1 1 2 0 0.03
7431 Angel Mccullough 1 1 2 0 0.03
7432 Anna Woodard 1 1 2 0 0.03
7433 Annetta Fanucci 1 1 2 0 0.03
7434 Ashley Powers 1 1 2 0 0.03
7435 Astrid Weinhold 1 1 2 0 0.03
7436 Aurore Leroux 1 1 2 0 0.03
7437 Austin Adams 1 1 2 0 0.03
7438 Barbara Russell 1 1 2 0 0.03
7439 Belén de Trillo 1 1 2 0 0.03
7440 Betty Vega 1 1 2 0 0.03
7441 Bhamini Luthra 1 1 2 0 0.03
7442 Božena Dušková 1 1 2 0 0.03
7443 Bradley Reeves Jr. 1 1 2 0 0.03
7444 Brady Brown 1 1 2 0 0.03
7445 Brandon Horton 1 1 2 0 0.03
7446 Brandy Chung PhD 1 1 2 0 0.03
7447 Brett Bond 1 1 2 0 0.03
7448 Brett Whitehead 1 1 2 0 0.03
7449 Brian Melton 1 1 2 0 0.03
7450 Brian Mitchell 1 1 2 0 0.03
7451 Brian Moody 1 1 2 0 0.03
7452 Brian Price 1 1 2 0 0.03
7453 Brittany Horn 1 1 2 0 0.03
7454 Bruce Hernandez 1 1 2 0 0.03
7455 Bryan Hale 1 1 2 0 0.03
7456 Candace Anderson 1 1 2 0 0.03
7457 Capucine Martin 1 1 2 0 0.03
7458 Cas Everde 1 1 2 0 0.03
7459 Catalina Borja Barbero 1 1 2 0 0.03
7460 Catherine Mcdonald 1 1 2 0 0.03
7461 Cecilia Parejo Carrillo 1 1 2 0 0.03
7462 Cecilia Scholz 1 1 2 0 0.03
7463 Chantal Gomes 1 1 2 0 0.03
7464 Chantal-Geneviève Fournier 1 1 2 0 0.03
7465 Chao Mo 1 1 2 0 0.03
7466 Charles Jordan 1 1 2 0 0.03
7467 Charles Norman 1 1 2 0 0.03
7468 Charles Smith 1 1 2 0 0.03
7469 Charles Spears 1 1 2 0 0.03
7470 Charlotte Marchand 1 1 2 0 0.03
7471 Charvi Taneja 1 1 2 0 0.03
7472 Cheryl Wiley 1 1 2 0 0.03
7473 Chiyo Ota 1 1 2 0 0.03
7474 Christina Dalton 1 1 2 0 0.03
7475 Christine Mendoza 1 1 2 0 0.03
7476 Christopher Hardy 1 1 2 0 0.03
7477 Christopher Martin 1 1 2 0 0.03
7478 Claude Jean 1 1 2 0 0.03
7479 Cody Mathis 1 1 2 0 0.03
7480 Corey Ramirez 1 1 2 0 0.03
7481 Corinne Leleu 1 1 2 0 0.03
7482 Curt Wieloch 1 1 2 0 0.03
7483 Cynthia Hoover 1 1 2 0 0.03
7484 Cyprian Perczak 1 1 2 0 0.03
7485 Dale Bell 1 1 2 0 0.03
7486 Damini Wable 1 1 2 0 0.03
7487 Daniel Watson 1 1 2 0 0.03
7488 Danielle Moore 1 1 2 0 0.03
7489 Dariusz Madera 1 1 2 0 0.03
7490 Darshit Chopra 1 1 2 0 0.03
7491 David Gibson 1 1 2 0 0.03
7492 David Singleton 1 1 2 0 0.03
7493 David Sloan 1 1 2 0 0.03
7494 Dawn Kelly 1 1 2 0 0.03
7495 Debra Wolfe 1 1 2 0 0.03
7496 Derrick Scott 1 1 2 0 0.03
7497 Devansh Bakshi 1 1 2 0 0.03
7498 Divit Behl 1 1 2 0 0.03
7499 Donna Ellison 1 1 2 0 0.03
7500 Douglas Roberts 1 1 2 0 0.03
7501 Dustin Smith 1 1 2 0 0.03
7502 Edith Schaaf 1 1 2 0 0.03
7503 Edward Collier 1 1 2 0 0.03
7504 Edward Kirby 1 1 2 0 0.03
7505 Edward Kraushaar 1 1 2 0 0.03
7506 Elizabeth Mccann 1 1 2 0 0.03
7507 Elladio Badoer-Beccaria 1 1 2 0 0.03
7508 Emily Arnold 1 1 2 0 0.03
7509 Emily Werner 1 1 2 0 0.03
7510 Emin Mentzel 1 1 2 0 0.03
7511 Emma Ruiz 1 1 2 0 0.03
7512 Erica Shaffer 1 1 2 0 0.03
7513 Erika Nguyen 1 1 2 0 0.03
7514 Eshani Deep 1 1 2 0 0.03
7515 Eugene Powell 1 1 2 0 0.03
7516 Eugenie Scheel 1 1 2 0 0.03
7517 Evangelina Bautista Catalá 1 1 2 0 0.03
7518 Evi Dietz 1 1 2 0 0.03
7519 Fang Cai 1 1 2 0 0.03
7520 Farhan Seshadri 1 1 2 0 0.03
7521 Fenne Ferran 1 1 2 0 0.03
7522 Frank Green 1 1 2 0 0.03
7523 Frau Oxana Jüttner 1 1 2 0 0.03
7524 Frau Raphaela Bohnbach 1 1 2 0 0.03
7525 Friedrich Eberhardt 1 1 2 0 0.03
7526 Gaetano Hartmann 1 1 2 0 0.03
7527 George Lloyd 1 1 2 0 0.03
7528 Georges Delorme 1 1 2 0 0.03
7529 Gerald Shepherd 1 1 2 0 0.03
7530 Gina Lehmann-Süßebier 1 1 2 0 0.03
7531 Goffredo Cheda 1 1 2 0 0.03
7532 Graciana Sacristán Saura 1 1 2 0 0.03
7533 Gregory Morris 1 1 2 0 0.03
7534 Grzegorz Pacocha 1 1 2 0 0.03
7535 Gudrun Atzler 1 1 2 0 0.03
7536 Guiying Luo 1 1 2 0 0.03
7537 Hailey Andrews 1 1 2 0 0.03
7538 Hanne Kambs 1 1 2 0 0.03
7539 Hans-Jörg Lehmann 1 1 2 0 0.03
7540 Harriet Wilms-Segebahn 1 1 2 0 0.03
7541 Harro Gerlach 1 1 2 0 0.03
7542 Haruka Kobayashi 1 1 2 0 0.03
7543 Heather Hanna 1 1 2 0 0.03
7544 Heather Hardy 1 1 2 0 0.03
7545 Heidi Perez 1 1 2 0 0.03
7546 Heidi Smith 1 1 2 0 0.03
7547 Helene van der Dussen 1 1 2 0 0.03
7548 Henri Da Costa 1 1 2 0 0.03
7549 Henri Maillard 1 1 2 0 0.03
7550 Herr Heinz-Gerd Schleich 1 1 2 0 0.03
7551 Hilmar Budig-Seip 1 1 2 0 0.03
7552 Himmat Shroff 1 1 2 0 0.03
7553 Hiroshi Yamamoto 1 1 2 0 0.03
7554 Holger Trupp 1 1 2 0 0.03
7555 Honoré Leduc 1 1 2 0 0.03
7556 Honoré-Alexandre Vidal 1 1 2 0 0.03
7557 Howard Taylor 1 1 2 0 0.03
7558 Hunter Green 1 1 2 0 0.03
7559 Ian Santos 1 1 2 0 0.03
7560 Ildefonso Aragón Guerra 1 1 2 0 0.03
7561 Imelda Reising-Franke 1 1 2 0 0.03
7562 Isaac Huet du Cousin 1 1 2 0 0.03
7563 Isaiah Morales 1 1 2 0 0.03
7564 Jafet Páez-Montenegro 1 1 2 0 0.03
7565 James Beard 1 1 2 0 0.03
7566 James Ellis 1 1 2 0 0.03
7567 James George 1 1 2 0 0.03
7568 James Hopkins 1 1 2 0 0.03
7569 James Li 1 1 2 0 0.03
7570 James Odom 1 1 2 0 0.03
7571 Jamie Castro 1 1 2 0 0.03
7572 Janet Aguilar 1 1 2 0 0.03
7573 Janet Hunter 1 1 2 0 0.03
7574 Janin Hartmann 1 1 2 0 0.03
7575 Jason Ali 1 1 2 0 0.03
7576 Jason Robles 1 1 2 0 0.03
7577 Jason Vaughn 1 1 2 0 0.03
7578 Jeanette Carey 1 1 2 0 0.03
7579 Jeanette Wagner 1 1 2 0 0.03
7580 Jennifer Tate 1 1 2 0 0.03
7581 Jeremi Głuszko 1 1 2 0 0.03
7582 Jeremi Piórek 1 1 2 0 0.03
7583 Jessica Esparza 1 1 2 0 0.03
7584 Jhanvi Krishnamurthy 1 1 2 0 0.03
7585 Jie Hou 1 1 2 0 0.03
7586 Jie Peng 1 1 2 0 0.03
7587 Jie Qiu 1 1 2 0 0.03
7588 Jill Smith 1 1 2 0 0.03
7589 John Buchanan 1 1 2 0 0.03
7590 John Maldonado 1 1 2 0 0.03
7591 Jonathan Jones Jr. 1 1 2 0 0.03
7592 Jordan Hopkins 1 1 2 0 0.03
7593 Joseph Escobar 1 1 2 0 0.03
7594 Joseph Hart 1 1 2 0 0.03
7595 Joseph Whitaker 1 1 2 0 0.03
7596 Jules Lemonnier 1 1 2 0 0.03
7597 Julia Berry 1 1 2 0 0.03
7598 Julia Davis 1 1 2 0 0.03
7599 Julie Gonzalez 1 1 2 0 0.03
7600 Julie Griffin 1 1 2 0 0.03
7601 Julie Nielsen 1 1 2 0 0.03
7602 Julie Thomas 1 1 2 0 0.03
7603 Jun Zhong 1 1 2 0 0.03
7604 Kaitlyn Brown 1 1 2 0 0.03
7605 Kaitlyn Castaneda PhD 1 1 2 0 0.03
7606 Kamila Bobko 1 1 2 0 0.03
7607 Karl Bruder 1 1 2 0 0.03
7608 Karla Moreno 1 1 2 0 0.03
7609 Katelyn Malone 1 1 2 0 0.03
7610 Katherine Tanner 1 1 2 0 0.03
7611 Kathleen Fowler 1 1 2 0 0.03
7612 Katy Spieß 1 1 2 0 0.03
7613 Kayla Martinez 1 1 2 0 0.03
7614 Kelly Barrett 1 1 2 0 0.03
7615 Kelly Clayton 1 1 2 0 0.03
7616 Kenneth Hall 1 1 2 0 0.03
7617 Kenneth Lopez 1 1 2 0 0.03
7618 Kevin Thomas 1 1 2 0 0.03
7619 Kimberly Porter 1 1 2 0 0.03
7620 Klaus-Werner Löchel 1 1 2 0 0.03
7621 Kristin Shepard 1 1 2 0 0.03
7622 Krystal Reese 1 1 2 0 0.03
7623 Kyle Lawrence 1 1 2 0 0.03
7624 Larry Bailey 1 1 2 0 0.03
7625 Laura Carson 1 1 2 0 0.03
7626 Laurie Anderson 1 1 2 0 0.03
7627 Lei Chang 1 1 2 0 0.03
7628 Lei Dong 1 1 2 0 0.03
7629 Lei Xu 1 1 2 0 0.03
7630 Li Cao 1 1 2 0 0.03
7631 Li Su 1 1 2 0 0.03
7632 Lidia Dörschner-Käster 1 1 2 0 0.03
7633 Linda Bailey 1 1 2 0 0.03
7634 Liwia Mitera 1 1 2 0 0.03
7635 Loredana Bertolucci 1 1 2 0 0.03
7636 Loretta Börner 1 1 2 0 0.03
7637 Lori Montgomery 1 1 2 0 0.03
7638 Léon Lemaire 1 1 2 0 0.03
7639 Maaya Hayashi 1 1 2 0 0.03
7640 Maaya Suzuki 1 1 2 0 0.03
7641 Madeline Gentry 1 1 2 0 0.03
7642 Manjari Varty 1 1 2 0 0.03
7643 Manuel Morrison 1 1 2 0 0.03
7644 Mar Abril Balaguer 1 1 2 0 0.03
7645 Margaret Mueller 1 1 2 0 0.03
7646 Margot Adam 1 1 2 0 0.03
7647 Marika Rörricht 1 1 2 0 0.03
7648 Marine Goncalves 1 1 2 0 0.03
7649 Martha Patrick 1 1 2 0 0.03
7650 Martin Anderson 1 1 2 0 0.03
7651 Martino Federico 1 1 2 0 0.03
7652 Mary Morales 1 1 2 0 0.03
7653 Mathilde Texier 1 1 2 0 0.03
7654 Matthew Davidson 1 1 2 0 0.03
7655 Matthew Phillips 1 1 2 0 0.03
7656 Maurycy Seidel 1 1 2 0 0.03
7657 Meghan Cortez 1 1 2 0 0.03
7658 Melanie Heß 1 1 2 0 0.03
7659 Michael Cole 1 1 2 0 0.03
7660 Michael Grant 1 1 2 0 0.03
7661 Michael Jackson 1 1 2 0 0.03
7662 Michael Roberts 1 1 2 0 0.03
7663 Michael Snyder 1 1 2 0 0.03
7664 Michael White 1 1 2 0 0.03
7665 Michaela Brooks 1 1 2 0 0.03
7666 Michelle Castro 1 1 2 0 0.03
7667 Michelle Gilbert 1 1 2 0 0.03
7668 Michelle Herrera 1 1 2 0 0.03
7669 Michelle Russo 1 1 2 0 0.03
7670 Michelle Shields 1 1 2 0 0.03
7671 Mikako Saito 1 1 2 0 0.03
7672 Min Du 1 1 2 0 0.03
7673 Min Kang 1 1 2 0 0.03
7674 Ming Meng 1 1 2 0 0.03
7675 Miss Erin Dean 1 1 2 0 0.03
7676 Mitchell Mcdonald 1 1 2 0 0.03
7677 Monica Coleman 1 1 2 0 0.03
7678 Monica Hunt 1 1 2 0 0.03
7679 Monica Mason 1 1 2 0 0.03
7680 Monika Leonowicz 1 1 2 0 0.03
7681 Na Huang 1 1 2 0 0.03
7682 Nancy Franklin 1 1 2 0 0.03
7683 Nancy Myers 1 1 2 0 0.03
7684 Naoko Suzuki 1 1 2 0 0.03
7685 Narrer på Vagt 1 1 2 0 0.03
7686 Natan Molka 1 1 2 0 0.03
7687 Navya Jhaveri 1 1 2 0 0.03
7688 Navya Kala 1 1 2 0 0.03
7689 Nicholas Rasmussen 1 1 2 0 0.03
7690 Nicolas Rodriguez 1 1 2 0 0.03
7691 Nicole Valenzuela 1 1 2 0 0.03
7692 Nino Pometta 1 1 2 0 0.03
7693 Olivia Jarvis 1 1 2 0 0.03
7694 Onofre Chacón Jordá 1 1 2 0 0.03
7695 Ottilie Fiebig 1 1 2 0 0.03
7696 Paco Yáñez Bravo 1 1 2 0 0.03
7697 Parinaaz Krishnamurthy 1 1 2 0 0.03
7698 Patricia Cherry 1 1 2 0 0.03
7699 Patrick Herrera 1 1 2 0 0.03
7700 Patrizia Drewes 1 1 2 0 0.03
7701 Paul Kirk 1 1 2 0 0.03
7702 Philippine du Pottier 1 1 2 0 0.03
7703 Pierre David 1 1 2 0 0.03
7704 Ping Fang 1 1 2 0 0.03
7705 Rafał Helak 1 1 2 0 0.03
7706 Raymond Blackburn 1 1 2 0 0.03
7707 Raymond Gates 1 1 2 0 0.03
7708 Raymond Martin 1 1 2 0 0.03
7709 Rebecca Bishop 1 1 2 0 0.03
7710 Rebecca Byrd 1 1 2 0 0.03
7711 Recep Schüler 1 1 2 0 0.03
7712 Regina Vance 1 1 2 0 0.03
7713 Renee Carr 1 1 2 0 0.03
7714 Reyansh Barad 1 1 2 0 0.03
7715 Rhea Chatterjee 1 1 2 0 0.03
7716 Rhea Dhar 1 1 2 0 0.03
7717 Rhonda Guerrero 1 1 2 0 0.03
7718 Richard Kaiser 1 1 2 0 0.03
7719 Richard Mckenzie 1 1 2 0 0.03
7720 Richard Vazquez 1 1 2 0 0.03
7721 Richard Ward 1 1 2 0 0.03
7722 Ritvik Venkatesh 1 1 2 0 0.03
7723 Robert Fernandez 1 1 2 0 0.03
7724 Robert Fowler 1 1 2 0 0.03
7725 Robert Gonzales 1 1 2 0 0.03
7726 Robert Nelson 1 1 2 0 0.03
7727 Robert Renault 1 1 2 0 0.03
7728 Rodney Hayes 1 1 2 0 0.03
7729 Roksana Kurzak 1 1 2 0 0.03
7730 Rolando Paruta 1 1 2 0 0.03
7731 Ronald Esparza 1 1 2 0 0.03
7732 Rosa-Maria Beyer 1 1 2 0 0.03
7733 Rosenda Reyes Salazar 1 1 2 0 0.03
7734 Ryan Hamilton 1 1 2 0 0.03
7735 Ryan Richards 1 1 2 0 0.03
7736 Ryan Smith 1 1 2 0 0.03
7737 Ryohei Nakajima 1 1 2 0 0.03
7738 Ryosuke Nakamura 1 1 2 0 0.03
7739 Rüdiger Schinke 1 1 2 0 0.03
7740 Saira Bose 1 1 2 0 0.03
7741 Samantha Taylor 1 1 2 0 0.03
7742 Samiha Hayre 1 1 2 0 0.03
7743 Samuel Lewis 1 1 2 0 0.03
7744 Sandra Riley 1 1 2 0 0.03
7745 Sara Seguí Vallés 1 1 2 0 0.03
7746 Sara Sharaf 1 1 2 0 0.03
7747 Scott Gutierrez 1 1 2 0 0.03
7748 Shane Clark 1 1 2 0 0.03
7749 Shannon Castro 1 1 2 0 0.03
7750 Sharon Roberts 1 1 2 0 0.03
7751 Shawn Hart 1 1 2 0 0.03
7752 Shohei Yamashita 1 1 2 0 0.03
7753 Shota Mori 1 1 2 0 0.03
7754 Stephanie Benjamin 1 1 2 0 0.03
7755 Stephanie Brown 1 1 2 0 0.03
7756 Stephanie Hanson 1 1 2 0 0.03
7757 Stephen Chavez 1 1 2 0 0.03
7758 Steve Schottin 1 1 2 0 0.03
7759 Steven Williams 1 1 2 0 0.03
7760 Summer Bender 1 1 2 0 0.03
7761 Suzanne Ramos 1 1 2 0 0.03
7762 Sven Oude Heer 1 1 2 0 0.03
7763 Takuma Shimizu 1 1 2 0 0.03
7764 Tammy Crane 1 1 2 0 0.03
7765 Tao Fan 1 1 2 0 0.03
7766 Tara Becker 1 1 2 0 0.03
7767 Tara Kaul 1 1 2 0 0.03
7768 Taylor Poole 1 1 2 0 0.03
7769 Terry Baker 1 1 2 0 0.03
7770 Theres Roht 1 1 2 0 0.03
7771 Thomas Boyle 1 1 2 0 0.03
7772 Thomas Duncan 1 1 2 0 0.03
7773 Thomas Mccoy 1 1 2 0 0.03
7774 Thomas Ramirez 1 1 2 0 0.03
7775 Tiffany Wong 1 1 2 0 0.03
7776 Timothy Chen 1 1 2 0 0.03
7777 Timothy Haley 1 1 2 0 0.03
7778 Timothy Kelly 1 1 2 0 0.03
7779 Timothée Lefèvre 1 1 2 0 0.03
7780 Tobiasz Kargol 1 1 2 0 0.03
7781 Tom Kade 1 1 2 0 0.03
7782 Tracy Combs 1 1 2 0 0.03
7783 Tracy Vazquez 1 1 2 0 0.03
7784 Traude Salz 1 1 2 0 0.03
7785 Traudl Jacob 1 1 2 0 0.03
7786 Travis Schmitt 1 1 2 0 0.03
7787 Trevor Gonzales 1 1 2 0 0.03
7788 Tyler Smith 1 1 2 0 0.03
7789 Tülay Tlustek 1 1 2 0 0.03
7790 Valentine Jourdan du Schneider 1 1 2 0 0.03
7791 Valerio Zapata-Rico 1 1 2 0 0.03
7792 Vedika Dar 1 1 2 0 0.03
7793 Veer Raju 1 1 2 0 0.03
7794 Vidur Khatri 1 1 2 0 0.03
7795 William Bates 1 1 2 0 0.03
7796 William Kennedy 1 1 2 0 0.03
7797 William Warren 1 1 2 0 0.03
7798 Wojciech Ratka 1 1 2 0 0.03
7799 Xia Tao 1 1 2 0 0.03
7800 Xia Yan 1 1 2 0 0.03
7801 Xiulan Chen 1 1 2 0 0.03
7802 Xiuying Yi 1 1 2 0 0.03
7803 Yan Mo 1 1 2 0 0.03
7804 Yang Fu 1 1 2 0 0.03
7805 Yang Shi 1 1 2 0 0.03
7806 Yolanda Mendoza 1 1 2 0 0.03
7807 Yolanda Roy 1 1 2 0 0.03
7808 Yong Zhao 1 1 2 0 0.03
7809 Zain Datta 1 1 2 0 0.03
7810 Élise Moreno 1 1 2 0 0.03
7811 Anthony Clark 1 0 15 1 0.03
7812 Deborah Matthews 1 0 15 1 0.03
7813 Douglas Haynes 1 0 15 1 0.03
7814 Jochen Becker 1 0 15 1 0.03
7815 John Mitchell 1 0 15 1 0.03
7816 Rhonda Ward 1 0 15 1 0.03
7817 Stilla Seifert 1 0 15 1 0.03
7818 Tilly Eigenwillig 1 0 15 1 0.03
7819 Timothy Simpson 1 0 15 1 0.03
7820 Aaron Jones 1 1 1 0 0.03
7821 Abraham Becker 1 1 1 0 0.03
7822 Agnes Stahr 1 1 1 0 0.03
7823 Ahana Sarin 1 1 1 0 0.03
7824 Akemi Nishimura 1 1 1 0 0.03
7825 Akemi Suzuki 1 1 1 0 0.03
7826 Alan Castro 1 1 1 0 0.03
7827 Alan Smith 1 1 1 0 0.03
7828 Alan Yang 1 1 1 0 0.03
7829 Albert White 1 1 1 0 0.03
7830 Albrecht Scholtz 1 1 1 0 0.03
7831 Alejandro Brown 1 1 1 0 0.03
7832 Alexis Church 1 1 1 0 0.03
7833 Alphonse Berthelot 1 1 1 0 0.03
7834 Amanda Mcdonald 1 1 1 0 0.03
7835 Amelie Roht 1 1 1 0 0.03
7836 Ana Garrison 1 1 1 0 0.03
7837 Ana Wilson 1 1 1 0 0.03
7838 Andrea Ramirez 1 1 1 0 0.03
7839 Angelica Ruiz 1 1 1 0 0.03
7840 Anthony Larson 1 1 1 0 0.03
7841 Autumn Knight 1 1 1 0 0.03
7842 Baccio Caracciolo-Pelli 1 1 1 0 0.03
7843 Beata Budig 1 1 1 0 0.03
7844 Bianca Pugliese 1 1 1 0 0.03
7845 Brandon Waller 1 1 1 0 0.03
7846 Carlos Franco 1 1 1 0 0.03
7847 Carrie Morris 1 1 1 0 0.03
7848 Catherine Parker 1 1 1 0 0.03
7849 Charles Normand 1 1 1 0 0.03
7850 Charles Shaw 1 1 1 0 0.03
7851 Chelsea Mata 1 1 1 0 0.03
7852 Chiyo Nakajima 1 1 1 0 0.03
7853 Christopher Brown 1 1 1 0 0.03
7854 Christopher Wells 1 1 1 0 0.03
7855 Christy Hicks 1 1 1 0 0.03
7856 Concha Roselló Verdugo 1 1 1 0 0.03
7857 Cynthia Johnson 1 1 1 0 0.03
7858 Cynthia Pierce 1 1 1 0 0.03
7859 Cyprian Goss 1 1 1 0 0.03
7860 DRIP$ETTA 1 1 1 0 0.03
7861 Darshit Gandhi 1 1 1 0 0.03
7862 Darshit Ratta 1 1 1 0 0.03
7863 David Campbell 1 1 1 0 0.03
7864 Deborah Cruz 1 1 1 0 0.03
7865 Deborah Young 1 1 1 0 0.03
7866 Debra Jones 1 1 1 0 0.03
7867 Devansh De 1 1 1 0 0.03
7868 Dhanuk Mane 1 1 1 0 0.03
7869 Diana Wilkinson 1 1 1 0 0.03
7870 Divyansh Sehgal 1 1 1 0 0.03
7871 Dustin Maxwell 1 1 1 0 0.03
7872 Elizabeth Montgomery 1 1 1 0 0.03
7873 Emily Hudson 1 1 1 0 0.03
7874 Enzio Caffarelli 1 1 1 0 0.03
7875 Erin Martinez 1 1 1 0 0.03
7876 Ethan Wiley 1 1 1 0 0.03
7877 Eulalia Patiño Ballester 1 1 1 0 0.03
7878 Evie Feenstra 1 1 1 0 0.03
7879 Fateh Bhatt 1 1 1 0 0.03
7880 Folkert Schmidtke 1 1 1 0 0.03
7881 Francisco Säuberlich 1 1 1 0 0.03
7882 Franck du Mercier 1 1 1 0 0.03
7883 Gabrielle Brady 1 1 1 0 0.03
7884 Gaja Powierża 1 1 1 0 0.03
7885 Gary Wilson 1 1 1 0 0.03
7886 George Bailey 1 1 1 0 0.03
7887 Gianmarco Verga 1 1 1 0 0.03
7888 Giovanna Bongiorno 1 1 1 0 0.03
7889 Giulietta Parini 1 1 1 0 0.03
7890 Grant Bruce 1 1 1 0 0.03
7891 Gregory Evans 1 1 1 0 0.03
7892 Guiying Mao 1 1 1 0 0.03
7893 Hannah Richard 1 1 1 0 0.03
7894 Hans-Joachim Seip 1 1 1 0 0.03
7895 Heather Davis 1 1 1 0 0.03
7896 Heinz-Georg Bolzmann 1 1 1 0 0.03
7897 Hilma Wirth 1 1 1 0 0.03
7898 Hortense Peron 1 1 1 0 0.03
7899 Hrishita Deep 1 1 1 0 0.03
7900 Hunar Dugal 1 1 1 0 0.03
7901 Imke Hartmann 1 1 1 0 0.03
7902 Indrajit Chana 1 1 1 0 0.03
7903 Ira Gole 1 1 1 0 0.03
7904 Ishaan Bal 1 1 1 0 0.03
7905 Isla Quinn 1 1 1 0 0.03
7906 Ivana Dhillon 1 1 1 0 0.03
7907 Jacek Kazimierczuk 1 1 1 0 0.03
7908 Jacob Bryant 1 1 1 0 0.03
7909 Jacob Gallagher 1 1 1 0 0.03
7910 James Gibbs 1 1 1 0 0.03
7911 James Harvey 1 1 1 0 0.03
7912 Janet Powers 1 1 1 0 0.03
7913 Janette Reuter 1 1 1 0 0.03
7914 Janice Villarreal 1 1 1 0 0.03
7915 Janna Holt 1 1 1 0 0.03
7916 Jason Mcpherson 1 1 1 0 0.03
7917 Jason Owens 1 1 1 0 0.03
7918 Jayan Barman 1 1 1 0 0.03
7919 Jeff Lewis 1 1 1 0 0.03
7920 Jeffrey Garrett 1 1 1 0 0.03
7921 Jeffrey Morgan 1 1 1 0 0.03
7922 Jenna Casey 1 1 1 0 0.03
7923 Jennifer Coleman 1 1 1 0 0.03
7924 Jennifer Ferguson 1 1 1 0 0.03
7925 Jessica Carney 1 1 1 0 0.03
7926 Jessica Garcia 1 1 1 0 0.03
7927 Jie Guo 1 1 1 0 0.03
7928 Jimmy Brown 1 1 1 0 0.03
7929 Jivin Wali 1 1 1 0 0.03
7930 Jodi Salazar 1 1 1 0 0.03
7931 Joe Mccarthy 1 1 1 0 0.03
7932 Jonathan Bishop 1 1 1 0 0.03
7933 Jonathan Thompson 1 1 1 0 0.03
7934 Jorge Atkinson 1 1 1 0 0.03
7935 Josep Nicanor Salcedo Arroyo 1 1 1 0 0.03
7936 Joseph Adams 1 1 1 0 0.03
7937 Joseph Contreras 1 1 1 0 0.03
7938 Joseph Wright 1 1 1 0 0.03
7939 Joshua Morrison 1 1 1 0 0.03
7940 Juan Qin 1 1 1 0 0.03
7941 Juan Yi 1 1 1 0 0.03
7942 Julianna Mytnik 1 1 1 0 0.03
7943 Julie Flores 1 1 1 0 0.03
7944 Julita Łaciak 1 1 1 0 0.03
7945 Juliusz Cierpisz 1 1 1 0 0.03
7946 Juliusz Hawrylak 1 1 1 0 0.03
7947 Kabir Dutta 1 1 1 0 0.03
7948 Karol Szczechowicz 1 1 1 0 0.03
7949 Kelly Briggs 1 1 1 0 0.03
7950 Kenichi Suzuki 1 1 1 0 0.03
7951 Kenneth Guerra 1 1 1 0 0.03
7952 Kenneth Ruiz 1 1 1 0 0.03
7953 Kevin Ayala 1 1 1 0 0.03
7954 Kevin Gilbert 1 1 1 0 0.03
7955 Kim Craig 1 1 1 0 0.03
7956 Kimaya Chaudhuri 1 1 1 0 0.03
7957 Kimberly Jones 1 1 1 0 0.03
7958 Kumiko Sato 1 1 1 0 0.03
7959 Kyle Cook 1 1 1 0 0.03
7960 Kyle Jackson 1 1 1 0 0.03
7961 Leroy Reeves 1 1 1 0 0.03
7962 Li Wang 1 1 1 0 0.03
7963 Lina Rensi 1 1 1 0 0.03
7964 Lisa Johnson 1 1 1 0 0.03
7965 Lori Schmidt 1 1 1 0 0.03
7966 Luc van Poppel-Hulskes 1 1 1 0 0.03
7967 Lucas Lenoir du Colin 1 1 1 0 0.03
7968 Maks Bodek 1 1 1 0 0.03
7969 Maks Grębowiec 1 1 1 0 0.03
7970 Marcin Jeżyk 1 1 1 0 0.03
7971 Marion Hellwig-Berger 1 1 1 0 0.03
7972 Mark Singleton 1 1 1 0 0.03
7973 Mary Frazier 1 1 1 0 0.03
7974 Mary Gordon 1 1 1 0 0.03
7975 Mary Mies 1 1 1 0 0.03
7976 Mary Williams 1 1 1 0 0.03
7977 Matthew Tran 1 1 1 0 0.03
7978 Max Brown 1 1 1 0 0.03
7979 Mckenzie Cuevas 1 1 1 0 0.03
7980 Mehul Varty 1 1 1 0 0.03
7981 Melanie Browning PhD 1 1 1 0 0.03
7982 Melissa Watson 1 1 1 0 0.03
7983 Michael Farrell 1 1 1 0 0.03
7984 Michael Lin 1 1 1 0 0.03
7985 Michelle Montoya 1 1 1 0 0.03
7986 Miluše Malá 1 1 1 0 0.03
7987 Min Xiao 1 1 1 0 0.03
7988 Na Ding 1 1 1 0 0.03
7989 Na Tao 1 1 1 0 0.03
7990 Natasha Wood 1 1 1 0 0.03
7991 Nathan Hernandez 1 1 1 0 0.03
7992 Nathaniel Fisher 1 1 1 0 0.03
7993 Nicholas Pope 1 1 1 0 0.03
7994 Nicolai Caspar 1 1 1 0 0.03
7995 Nino Canova-Esposito 1 1 1 0 0.03
7996 Ottomar Spieß 1 1 1 0 0.03
7997 Pamela Moran 1 1 1 0 0.03
7998 Pamela Schultz 1 1 1 0 0.03
7999 Pari Sharaf 1 1 1 0 0.03
8000 Paul Ashley 1 1 1 0 0.03
8001 Paul Contreras 1 1 1 0 0.03
8002 Phillip Dennis 1 1 1 0 0.03
8003 Ping Zhao 1 1 1 0 0.03
8004 Piya Ravel 1 1 1 0 0.03
8005 Qiang Hou 1 1 1 0 0.03
8006 Rachel Lewis 1 1 1 0 0.03
8007 Richard Mathieu de Perrin 1 1 1 0 0.03
8008 Ritvik Taneja 1 1 1 0 0.03
8009 Riya Dhingra 1 1 1 0 0.03
8010 Robert Bass 1 1 1 0 0.03
8011 Robert Strickland 1 1 1 0 0.03
8012 Ronald Barnett 1 1 1 0 0.03
8013 Rosa Flaiano 1 1 1 0 0.03
8014 Ruben Green 1 1 1 0 0.03
8015 Ryan Howard 1 1 1 0 0.03
8016 Ryan King 1 1 1 0 0.03
8017 Ryan Murphy 1 1 1 0 0.03
8018 Sabrina Hughes 1 1 1 0 0.03
8019 Samaira Borah 1 1 1 0 0.03
8020 Samarth Saraf 1 1 1 0 0.03
8021 Sandra Horton 1 1 1 0 0.03
8022 Sandra Reyes 1 1 1 0 0.03
8023 Sara Levy 1 1 1 0 0.03
8024 Sarah Anderson 1 1 1 0 0.03
8025 Scott Kemp 1 1 1 0 0.03
8026 Shannon Schaefer 1 1 1 0 0.03
8027 Shawn Saunders 1 1 1 0 0.03
8028 Shelia Miller 1 1 1 0 0.03
8029 Shlok Cherian 1 1 1 0 0.03
8030 Shota Ito 1 1 1 0 0.03
8031 Simone Maurice 1 1 1 0 0.03
8032 Stacey Perry 1 1 1 0 0.03
8033 Stacy Davenport 1 1 1 0 0.03
8034 Stephanie Potter 1 1 1 0 0.03
8035 Susan Romero 1 1 1 0 0.03
8036 Sylwia Hołota 1 1 1 0 0.03
8037 Tanya Garrett 1 1 1 0 0.03
8038 Tara Dennis 1 1 1 0 0.03
8039 Tara Deshmukh 1 1 1 0 0.03
8040 Tara Ghose 1 1 1 0 0.03
8041 Tara Rivera 1 1 1 0 0.03
8042 Teófilo Flor Gras 1 1 1 0 0.03
8043 The Lunar Manifesto 1 1 1 0 0.03
8044 Thomas Carson 1 1 1 0 0.03
8045 Thomas Jones 1 1 1 0 0.03
8046 Tiffany Fitzpatrick 1 1 1 0 0.03
8047 Tiya Desai 1 1 1 0 0.03
8048 Tricia Yoder 1 1 1 0 0.03
8049 Tsubasa Sasaki 1 1 1 0 0.03
8050 Umang Bala 1 1 1 0 0.03
8051 Valentina Geraci 1 1 1 0 0.03
8052 Vincent Berg 1 1 1 0 0.03
8053 Vivaan Kala 1 1 1 0 0.03
8054 Vlastimil Bílek 1 1 1 0 0.03
8055 Wei Jiang 1 1 1 0 0.03
8056 William Fritz 1 1 1 0 0.03
8057 William Garner 1 1 1 0 0.03
8058 William Navarro 1 1 1 0 0.03
8059 William Ruiz 1 1 1 0 0.03
8060 William Tran 1 1 1 0 0.03
8061 Xia Huang 1 1 1 0 0.03
8062 Xiulan Xiao 1 1 1 0 0.03
8063 Yan Zeng 1 1 1 0 0.03
8064 Yan Zhang 1 1 1 0 0.03
8065 Yang Gong 1 1 1 0 0.03
8066 Yasmin Vig 1 1 1 0 0.03
8067 Yasuhiro Nakamura 1 1 1 0 0.03
8068 Yong Wan 1 1 1 0 0.03
8069 Emmanuelle Leleu 1 0 14 1 0.03
8070 Gilles Peltier-Rocher 1 0 14 1 0.03
8071 Adrian Bridges 1 0 10 2 0.03
8072 Annalisa Saragat-Pininfarina 1 0 10 2 0.03
8073 Christian Scott 1 0 10 2 0.03
8074 Lolita Speri 1 0 10 2 0.03
8075 Margaret Brun 1 0 10 2 0.03
8076 Vanessa Leon 1 0 10 2 0.03
8077 Alex Steele 1 1 0 0 0.03
8078 Amanda Powell 1 1 0 0 0.03
8079 Andrea Green 1 1 0 0 0.03
8080 Anna Reyes 1 1 0 0 0.03
8081 Caroline Lloyd 1 1 0 0 0.03
8082 Christopher Glover 1 1 0 0 0.03
8083 Christopher Ward 1 1 0 0 0.03
8084 Filippa Piane 1 1 0 0 0.03
8085 Frederik Hartung 1 1 0 0 0.03
8086 Heidi Berry 1 1 0 0 0.03
8087 Hridaan Rout 1 1 0 0 0.03
8088 James Goodwin 1 1 0 0 0.03
8089 Jeffery Reese 1 1 0 0 0.03
8090 Jeffrey Middleton 1 1 0 0 0.03
8091 Jessica Russell 1 1 0 0 0.03
8092 Joan Taylor 1 1 0 0 0.03
8093 John Baxter 1 1 0 0 0.03
8094 Julie Larson 1 1 0 0 0.03
8095 Katherine Davis 1 1 0 0 0.03
8096 Katherine Stevens 1 1 0 0 0.03
8097 Klara Bisaga 1 1 0 0 0.03
8098 Konrad Koźlak 1 1 0 0 0.03
8099 Laura Cohen 1 1 0 0 0.03
8100 Lisa Maxwell 1 1 0 0 0.03
8101 Marit Thies 1 1 0 0 0.03
8102 Matthew Sanchez 1 1 0 0 0.03
8103 Michał Flasza 1 1 0 0 0.03
8104 Nanami Sakamoto 1 1 0 0 0.03
8105 Nicholas Johnson 1 1 0 0 0.03
8106 Radosław Krzyk 1 1 0 0 0.03
8107 Susan Jones 1 1 0 0 0.03
8108 Svetlana Freudenberger 1 1 0 0 0.03
8109 Tadeusz Kieszek 1 1 0 0 0.03
8110 Terry Olson 1 1 0 0 0.03
8111 Vidur Dasgupta 1 1 0 0 0.03
8112 Herr Heinfried Kraushaar 1 0 9 2 0.03
8113 Alison Nelson 1 0 8 2 0.03
8114 Eric Gomez 1 0 8 2 0.03
8115 Kimberly Stevens 1 0 8 2 0.03
8116 Xiuying Chen 1 0 8 2 0.03
8117 Bobby Hester 1 0 11 1 0.02
8118 Brian Holmes 1 0 11 1 0.02
8119 Jan Jaśko 1 0 11 1 0.02
8120 Konrad Szpyt 1 0 11 1 0.02
8121 Shari Gomez 1 0 11 1 0.02
8122 Freddy Dörr 1 0 7 2 0.02
8123 Gina Lehmann-Nohlmans 1 0 7 2 0.02
8124 Hans-Ludwig Käster 1 0 7 2 0.02
8125 Niels Hering 1 0 7 2 0.02
8126 Anni Bauer 1 0 10 1 0.02
8127 Cornelius Kabus 1 0 10 1 0.02
8128 Friedo Eberhardt 1 0 10 1 0.02
8129 Heinz-Josef Ritter 1 0 10 1 0.02
8130 Sepp Köhler 1 0 10 1 0.02
8131 Vadim Killer 1 0 10 1 0.02
8132 Amanda Davidson 1 0 13 0 0.02
8133 Barbara Hall 1 0 13 0 0.02
8134 Cassandra Norris 1 0 13 0 0.02
8135 Danielle Peters 1 0 13 0 0.02
8136 Katherine Castillo 1 0 13 0 0.02
8137 Kelli Rojas 1 0 13 0 0.02
8138 Mary Gonzalez 1 0 13 0 0.02
8139 Rebecca Taylor 1 0 13 0 0.02
8140 Tonya Andrews 1 0 13 0 0.02
8141 Casey Green 1 0 9 1 0.02
8142 Frau Sybille Becker 1 0 9 1 0.02
8143 Fritz Schuster 1 0 9 1 0.02
8144 John Goodwin 1 0 9 1 0.02
8145 Miranda Page 1 0 9 1 0.02
8146 Patricia Rodriguez 1 0 9 1 0.02
8147 Radmila Stey-Kambs 1 0 9 1 0.02
8148 Rick House 1 0 9 1 0.02
8149 Ronald Rice 1 0 9 1 0.02
8150 Susan Rogers 1 0 9 1 0.02
8151 Aarav Chada 1 0 8 1 0.02
8152 Jennifer Alexander 1 0 8 1 0.02
8153 Kristina Lang 1 0 8 1 0.02
8154 Lauren Steele 1 0 8 1 0.02
8155 Lori Young 1 0 8 1 0.02
8156 Sean Riggs 1 0 8 1 0.02
8157 Tiffany Cain 1 0 8 1 0.02
8158 Camilla Sontag 1 0 7 1 0.02
8159 Irmtrud Schmiedecke 1 0 7 1 0.02
8160 Rosalie Bonbach 1 0 7 1 0.02
8161 Shane Davis 1 0 7 1 0.02
8162 Xia Dong 1 0 7 1 0.02
8163 Adrien Gonzalez 1 0 6 1 0.02
8164 Anastasie Auger 1 0 6 1 0.02
8165 Georges-Grégoire Martin 1 0 6 1 0.02
8166 Bronze Tiger 1 0 5 1 0.01
8167 Bryce Webb 1 0 5 1 0.01
8168 Debra Richards 1 0 5 1 0.01
8169 Lisa Frost 1 0 5 1 0.01
8170 Michael Hall 1 0 5 1 0.01
8171 Andrew Oneill 1 0 8 0 0.01
8172 Beth Walters 1 0 8 0 0.01
8173 Jacob Wiek 1 0 8 0 0.01
8174 Jennifer Ruiz 1 0 8 0 0.01
8175 Nina Anders 1 0 8 0 0.01
8176 Rachel Luna 1 0 8 0 0.01
8177 Regina Watkins 1 0 8 0 0.01
8178 Sandra Holden 1 0 8 0 0.01
8179 Tabitha Jones 1 0 8 0 0.01
8180 Tyler Austin 1 0 8 0 0.01
8181 Alex Hernandez 1 0 4 1 0.01
8182 Cesar Nguyen 1 0 4 1 0.01
8183 Elizabeth Donaldson 1 0 4 1 0.01
8184 Kevin Russell 1 0 4 1 0.01
8185 Stanley Perez 1 0 4 1 0.01
8186 Sydney Mack 1 0 4 1 0.01
8187 Kristina Jones 1 0 7 0 0.01
8188 Claudia Cunningham 1 0 3 1 0.01
8189 James Palmer 1 0 3 1 0.01
8190 Janina Gronkiewicz 1 0 3 1 0.01
8191 Phillip Barnes 1 0 3 1 0.01
8192 Robert Hughes 1 0 3 1 0.01
8193 Tao Xue 1 0 6 0 0.01
8194 Jennifer Moore 1 0 2 1 0.01
8195 Benjamin Gutierrez 1 0 5 0 0.01
8196 Bradley Valencia 1 0 5 0 0.01
8197 Brittany Sanchez 1 0 5 0 0.01
8198 Charlene Collins 1 0 5 0 0.01
8199 Elizabeth Brennan 1 0 5 0 0.01
8200 Jason Davis 1 0 5 0 0.01
8201 Lisa Sparks 1 0 5 0 0.01
8202 Maria Rubio 1 0 5 0 0.01
8203 Patrick Wall 1 0 5 0 0.01
8204 Timothy Sanchez 1 0 5 0 0.01
8205 Andean Horizon 1 0 4 0 0.01
8206 B.D. Wolf 1 0 4 0 0.01
8207 Connor Juarez 1 0 4 0 0.01
8208 Darren Pineda 1 0 4 0 0.01
8209 Dishani Sundaram 1 0 4 0 0.01
8210 Doris Klemt-Junck 1 0 4 0 0.01
8211 Eon 1 0 4 0 0.01
8212 Gokul Kapur 1 0 4 0 0.01
8213 Indrans Vyas 1 0 4 0 0.01
8214 Isabella Johnson 1 0 4 0 0.01
8215 James Weber 1 0 4 0 0.01
8216 Jing Bai 1 0 4 0 0.01
8217 Lei Hao 1 0 4 0 0.01
8218 Lucas Smith 1 0 4 0 0.01
8219 Mark Dunlap 1 0 4 0 0.01
8220 Melissa Douglas 1 0 4 0 0.01
8221 Philippine Colin 1 0 4 0 0.01
8222 Riaan Kalla 1 0 4 0 0.01
8223 Saira Kala 1 0 4 0 0.01
8224 Sean Cameron 1 0 4 0 0.01
8225 Shota Suzuki 1 0 4 0 0.01
8226 The Hollow Vestige 1 0 4 0 0.01
8227 William Smith 1 0 4 0 0.01
8228 Elizabeth Leon 1 0 3 0 0.01
8229 Gregory Kane 1 0 3 0 0.01
8230 Günther Mude 1 0 3 0 0.01
8231 Jessica Coleman 1 0 3 0 0.01
8232 Joerg Weiß 1 0 3 0 0.01
8233 Kim Bishop 1 0 3 0 0.01
8234 Manuel Roberts 1 0 3 0 0.01
8235 Megan Stephenson 1 0 3 0 0.01
8236 Michele Orozco 1 0 3 0 0.01
8237 Molly Whitaker 1 0 3 0 0.01
8238 PRVISE 1 0 3 0 0.01
8239 Patricia Jackson 1 0 3 0 0.01
8240 Peter Byrd 1 0 3 0 0.01
8241 Reginald Stumpf 1 0 3 0 0.01
8242 William Ashley 1 0 3 0 0.01
8243 Alicia Simmons 1 0 2 0 0.00
8244 Anne Lefebvre 1 0 2 0 0.00
8245 Christopher Conway 1 0 2 0 0.00
8246 Dominique Letellier 1 0 2 0 0.00
8247 Heidi Dudley 1 0 2 0 0.00
8248 Isaac Brown 1 0 2 0 0.00
8249 Jonathan Morris 1 0 2 0 0.00
8250 Kimberly Walker 1 0 2 0 0.00
8251 Kristen Lucero 1 0 2 0 0.00
8252 Samantha Mcneil 1 0 2 0 0.00
8253 William Torres 1 0 2 0 0.00
8254 Echelon 1 0 1 0 0.00
8255 Gudula Riehl 1 0 1 0 0.00
8256 Helmtrud Mälzer-Paffrath 1 0 1 0 0.00
8257 Michael Potts 1 0 1 0 0.00
8258 Ping Duan 1 0 1 0 0.00
8259 Yang Tao 1 0 1 0 0.00
8260 John Hayes 1 0 0 0 0.00
8261 Jędrzej Przeniosło 1 0 0 0 0.00

4.2 Part 3a) Visualize the careers of three artists. Compare and contrast their rise in popularity and influence?

Based on the results of the analysis above, the top artists in each category will be selected.

  • Greatest Number of Music Released: Sailor Shift
  • Most Popular Artist (Highest Number of Notable Hits): Jay Walters
  • Most Influential Artist (Influence Artists and Collaborations), Most Music Influence & Highest Star Factor: Min Fu
Show Code
# Extract all creators' relevant data for scaling
all_scaled_data <- creator_rankings %>%
  select(total_songs, notable_hits, collaboration_influence_creator, influence_music)

# Scale all columns to 0–100
all_scaled <- as.data.frame(lapply(all_scaled_data, scales::rescale, to = c(0, 100)))

rownames(all_scaled) <- creator_rankings$creator_name

chosen_creator_1 = "Sailor Shift"

# Filter the data for Sailor Shift
creator_1_data <- creator_rankings %>%
  filter(creator_name == chosen_creator_1) %>%
  select(total_songs, notable_hits, collaboration_influence_creator, influence_music)

# Extract scaled values for Sailor Shift
creator_1_scaled <- all_scaled[chosen_creator_1, , drop = FALSE]

# Construct radar input with min and max for the chart frame
radar_matrix_1 <- rbind(
  rep(100, ncol(creator_1_scaled)),  # Max values
  rep(0, ncol(creator_1_scaled)),    # Min values
  creator_1_scaled                   # Actual values
)

# Plot Radar Chart
par(mar = c(2, 2, 4, 2), oma = c(1, 1, 3, 1))

radarchart(radar_matrix_1,
           axistype = 1,
           pcol = "#0027EA",
           pfcol = adjustcolor("#0027EA", alpha.f = 0.3),
           plwd = 2,
           cglcol = "grey",
           cglty = 1,
           cglwd = 0.8,
           axislabcol = "black",
           caxislabels = paste0(seq(0, 100, 25), "%"),
           vlcex = 0.85,
           title = paste("Star Profile: ", chosen_creator_1),
           calcex = 0.8,
           cex.main = 1.3,
           vlabels = c("Total Music", "Notable\nHits", "Artist Influ & Colab", "Music\nInfluenced"),
           centerzero = TRUE)

Show Code
chosen_creator_2 = "Jay Walters"

# Filter the data for Sailor Shift
creator_2_data <- creator_rankings %>%
  filter(creator_name == chosen_creator_2) %>%
  select(total_songs, notable_hits, collaboration_influence_creator, influence_music)

# Extract scaled values for Sailor Shift
creator_2_scaled <- all_scaled[chosen_creator_2, , drop = FALSE]

# Construct radar input with min and max for the chart frame
radar_matrix_2 <- rbind(
  rep(100, ncol(creator_2_scaled)),  # Max values
  rep(0, ncol(creator_2_scaled)),    # Min values
  creator_2_scaled                   # Actual values
)

# Plot Radar Chart
par(mar = c(2, 2, 4, 2), oma = c(1, 1, 3, 1))

radarchart(radar_matrix_2,
           axistype = 1,
           pcol = "#FF5757",
           pfcol = adjustcolor("#FF5757", alpha.f = 0.3),
           plwd = 2,
           cglcol = "grey",
           cglty = 1,
           cglwd = 0.8,
           axislabcol = "black",
           caxislabels = paste0(seq(0, 100, 25), "%"),
           vlcex = 0.85,
           title = paste("Star Profile: ", chosen_creator_2),
           calcex = 0.8,
           cex.main = 1.3,
           vlabels = c("Total Music", "Notable\nHits", "Artist Influ & Colab", "Music\nInfluenced"),
           centerzero = TRUE)

Show Code
chosen_creator_3 = "Min Fu"

# Filter the data for Sailor Shift
creator_3_data <- creator_rankings %>%
  filter(creator_name == chosen_creator_3) %>%
  select(total_songs, notable_hits, collaboration_influence_creator, influence_music)

# Extract scaled values for Sailor Shift
creator_3_scaled <- all_scaled[chosen_creator_3, , drop = FALSE]

# Construct radar input with min and max for the chart frame
radar_matrix_3 <- rbind(
  rep(100, ncol(creator_3_scaled)),  # Max values
  rep(0, ncol(creator_3_scaled)),    # Min values
  creator_3_scaled                   # Actual values
)

# Plot Radar Chart
par(mar = c(2, 2, 4, 2), oma = c(1, 1, 3, 1))

radarchart(radar_matrix_3,
           axistype = 1,
           pcol = "#A45200",
           pfcol = adjustcolor("#A45200", alpha.f = 0.3),
           plwd = 2,
           cglcol = "grey",
           cglty = 1,
           cglwd = 0.8,
           axislabcol = "black",
           caxislabels = paste0(seq(0, 100, 25), "%"),
           vlcex = 0.85,
           title = paste("Star Profile: ", chosen_creator_3),
           calcex = 0.8,
           cex.main = 1.3,
           vlabels = c("Total Music", "Notable\nHits", "Artist Influ & Colab", "Music\nInfluenced"),
           centerzero = TRUE)

4.2.1 Visualizing the career of Sailor Shift

Sailor Shift has released the most number of music (Song/Album).

Show Code
# Data Preparation

chosen_creator_1 = "Sailor Shift"

# Step 1: Get the node of the chosen creator
chosen_node_1 <- creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_1) %>%
  pull(creator_from) %>%
  unique()

# Step 2: Get the songs that the top creator produced
creator_songs_1 <- creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_1) %>%
  pull(song_to)

# Step 3: Get the songs they have influenced / artists they collaborate with
creators_songs_collaborate_influence_1 <- creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_1,
         infuence_music_collaborate != chosen_node_1) %>%
  pull(infuence_music_collaborate)

# Step 4: Get the songs they have influenced
creators_songs_influence_1 <- creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_1,
         infuence_music_collaborate != chosen_node_1,
         `Edge Colour` == "Influenced By") %>%
  pull(infuence_music_collaborate)

# Step 5: Get the influenced creators of the influenced songs
creators_songs_influence_creators_1 <- creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_1,
         influence_creator != chosen_node_1,
         !is.na(influence_creator),
         infuence_music_collaborate %in% creators_songs_influence_1) %>%
  pull(influence_creator)

all_nodes <- unique(c(chosen_node_1, 
                      creator_songs_1, 
                      creators_songs_collaborate_influence_1,
                      creators_songs_influence_creators_1))

# Create subgraph
sub_graph <- graph %>%
  filter(name %in% all_nodes)



# Visualisation

g <- sub_graph %>%
  ggraph(layout = "fr") + 
  geom_edge_fan(
    aes(
      edge_colour = `Edge Colour`,
      start_cap = circle(1, 'mm'),
      end_cap = circle(1, 'mm')
      ),
    arrow = arrow(length = unit(1, 'mm')),
    alpha = 0.3
  ) +
  geom_point_interactive(
    aes(
      x = x,
      y = y,
      data_id = name,
      colour = `Node Colour`,
      shape = `Node Type`,
      size = ifelse(node_name == chosen_creator_1, 3, 1),
      tooltip = case_when(
        `Node Type` == "Album" ~ sprintf(
          "%s<br/>%s<br/>Notable: %s<br/>(%s)", node_name, genre, notable, release_date
        ),
        `Node Type` == "Song" ~ sprintf(
          "%s<br/>%s<br/>Notable: %s<br/>(%s)<br/>Single: %s", node_name, genre, notable, release_date, single
        ),
        TRUE ~ sprintf("%s", node_name)
      )
    ),
    show.legend = c(size = FALSE)
  )+ 
  geom_node_text(
    aes(
      label = ifelse(node_name == chosen_creator_1, chosen_creator_1, NA)
    ),
    fontface = "bold",
    size = 2.5,
    colour = 'red',
    show.legend = FALSE
  ) +
  scale_shape_manual(
    name = "Node Type",
    values = c(
      "Album" = 16,
      "MusicalGroup" = 15,
      "Person" = 17,
      "Song" = 10
    )
  ) +
  scale_edge_colour_manual(
    name = "Edge Colour",
    values = c(
      `Creator Of` = "#47D45A",
      `Influenced By` = "#FF5757",
      `Member Of` = "#CF57FF"
    )
  ) +
  scale_colour_manual(
    name = "Node Colour",
    values = c(
      "Musician" = "grey50",
      "Oceanus Folk" = "#0027EA",
      "Other Genre" = "#A45200"
    )
  ) +
  theme_graph() +
  theme(legend.text = element_text(size = 6),
        legend.title = element_text(size = 9)) +
  scale_size_identity()

girafe(ggobj = g, width_svg = 7, height_svg = 6)
Show Code
# Data Preparation

# Step 1: Count number of music by release date
music_by_date_1 <- mc1_nodes_clean %>%
  filter(name %in% unique(creator_songs_1)) %>%
  count(release_date, name = "music_count") %>%
  arrange(release_date) %>%  # Ensure dates are in chronological order
  mutate(cumulative_count = cumsum(music_count))



# Visualisation

plot_ly(
  data = music_by_date_1,
  x = ~release_date,
  y = ~music_count,
  type = "bar",
  name = "Number of Music Releases",
  marker = list(color = "#2E3192"),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Released Date: ", release_date,
    "<br>Count: ", music_count
    )
  ) %>%
  add_trace(
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = "Cumulative Count",
    line = list(color = "black", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Released Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Yearly Music (Song/Album) Releases",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )
Show Code
# Data Preparation

# Step 1: Count number of notable music by release date
notable_music_by_date_1 <- mc1_nodes_clean %>%
  filter(name %in% unique(creator_songs_1), notable == TRUE) %>%
  count(release_date, name = "music_count") %>%
  arrange(release_date) %>%  # Ensure dates are in chronological order
  mutate(cumulative_count = cumsum(music_count))



# Visualisation

plot_ly(
  data = notable_music_by_date_1,
  x = ~release_date,
  y = ~music_count,
  type = "bar",
  name = "Number of Notable Music Releases",
  marker = list(color = "#2E3192"),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Released Date: ", release_date,
    "<br>Count: ", music_count
    )
  ) %>%
  add_trace(
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = "Cumulative Count",
    line = list(color = "black", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Released Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Yearly Notable Music (Song/Album) Releases",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )
Show Code
# Data Preparation

# Step 1: Count number of influenced artists by release date
influence_artists_by_date_1 <- creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_from %in% unique(chosen_node_1),
         influence_creator != unique(chosen_node_1)) %>%
  # Get unique artist-date pairs first
  distinct(influence_creator, influence_release_date) %>%
  # Find first influence date for each artist
  group_by(influence_creator) %>%
  summarize(
    first_influence_date = if(n() > 0) min(influence_release_date) else NA_real_,
    .groups = "drop"
  ) %>%
  # Count new artists by first influence date
  count(first_influence_date, name = "music_count") %>%
  arrange(first_influence_date) %>%
  rename(creator_release_date = first_influence_date) %>%
  # Calculate cumulative unique artists
  mutate(cumulative_count = cumsum(music_count))



# Visualisation

plot_ly(
  data = influence_artists_by_date_1,
  x = ~creator_release_date,
  y = ~music_count,
  type = "bar",
  name = "Number of Influenced Artists",
  marker = list(color = "#2E3192"),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Influence Date: ", creator_release_date,
    "<br>Count: ", music_count
    )
  ) %>%
  add_trace(
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = "Cumulative Count",
    line = list(color = "black", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Influence Date: ", creator_release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Annual Count of New Artist Influences & Collaborations",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )
Show Code
# Data Preparation

# Step 1: Count number of influenced music by release date
influence_song_by_date_1 <- mc1_nodes_clean %>%
  filter(name %in% unique(creators_songs_influence_1)) %>%
  count(release_date, name = "music_count") %>%
  arrange(release_date) %>%  # Ensure dates are in chronological order
  mutate(cumulative_count = cumsum(music_count))



# Visualisation

plot_ly(
  data = influence_song_by_date_1,
  x = ~release_date,
  y = ~music_count,
  type = "bar",
  name = "Number of Influenced Music",
  marker = list(color = "#2E3192"),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Influence Date: ", release_date,
    "<br>Count: ", music_count
    )
  ) %>%
  add_trace(
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = "Cumulative Count",
    line = list(color = "black", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Influence Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Yearly Music (Song/Album) Influence",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )
Note

Sailor Shift has no influence on Music creation

4.2.2 Visualizing the career of Jay Walters

Jay Walters has the highest number of notable hits.

Show Code
# Data Preparation

chosen_creator_2 = "Jay Walters"

# Step 1: Get the node of the chosen creator
chosen_node_2 <- creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_2) %>%
  pull(creator_from) %>%
  unique()

# Step 2: Get the songs that the top creator produced
creator_songs_2 <- creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_2) %>%
  pull(song_to)

# Step 3: Get the songs they have influenced / artists they collaborate with
creators_songs_collaborate_influence_2 <- creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_2) %>%
  pull(infuence_music_collaborate)

# Step 4: Get the songs they have influenced
creators_songs_influence_2 <- creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_2,
         `Edge Colour` == "Influenced By") %>%
  pull(infuence_music_collaborate)

# Step 5: Get the influenced creators of the influenced songs
creators_songs_influence_creators_2 <- creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_2,
         influence_creator != chosen_node_1,
         !is.na(influence_creator),
         infuence_music_collaborate %in% creators_songs_influence_2) %>%
  pull(influence_creator)

all_nodes <- unique(c(chosen_node_2, 
                      creator_songs_2, 
                      creators_songs_collaborate_influence_2,
                      creators_songs_influence_creators_2))

# Create subgraph
sub_graph <- graph %>%
  filter(name %in% all_nodes)



# Visualisation

g <- sub_graph %>%
  ggraph(layout = "fr") + 
  geom_edge_fan(
    aes(
      edge_colour = `Edge Colour`,
      start_cap = circle(1, 'mm'),
      end_cap = circle(1, 'mm')
      ),
    arrow = arrow(length = unit(1, 'mm')),
    alpha = 0.3
  ) +
  geom_point_interactive(
    aes(
      x = x,
      y = y,
      data_id = name,
      colour = `Node Colour`,
      shape = `Node Type`,
      size = ifelse(node_name == chosen_creator_2, 3, 1),
      tooltip = case_when(
        `Node Type` == "Album" ~ sprintf(
          "%s<br/>%s<br/>Notable: %s<br/>(%s)", node_name, genre, notable, release_date
        ),
        `Node Type` == "Song" ~ sprintf(
          "%s<br/>%s<br/>Notable: %s<br/>(%s)<br/>Single: %s", node_name, genre, notable, release_date, single
        ),
        TRUE ~ sprintf("%s", node_name)
      )
    ),
    show.legend = c(size = FALSE)
  )+ 
  geom_node_text(
    aes(
      label = ifelse(node_name == chosen_creator_2, chosen_creator_2, NA)
    ),
    fontface = "bold",
    size = 2.5,
    colour = 'red',
    show.legend = FALSE
  ) +
  scale_shape_manual(
    name = "Node Type",
    values = c(
      "Album" = 16,
      "MusicalGroup" = 15,
      "Person" = 17,
      "Song" = 10
    )
  ) +
  scale_edge_colour_manual(
    name = "Edge Colour",
    values = c(
      `Creator Of` = "#47D45A",
      `Influenced By` = "#FF5757",
      `Member Of` = "#CF57FF"
    )
  ) +
  scale_colour_manual(
    name = "Node Colour",
    values = c(
      "Musician" = "grey50",
      "Oceanus Folk" = "#0027EA",
      "Other Genre" = "#A45200"
    )
  ) +
  theme_graph() +
  theme(legend.text = element_text(size = 6),
        legend.title = element_text(size = 9)) +
  scale_size_identity()

girafe(ggobj = g, width_svg = 7, height_svg = 6)
Show Code
# Data Preparation

# Step 1: Count number of music by release date
music_by_date_2 <- mc1_nodes_clean %>%
  filter(name %in% unique(creator_songs_2)) %>%
  count(release_date, name = "music_count") %>%
  arrange(release_date) %>%  # Ensure dates are in chronological order
  mutate(cumulative_count = cumsum(music_count))



# Visualisation

plot_ly(
  data = music_by_date_2,
  x = ~release_date,
  y = ~music_count,
  type = "bar",
  name = "Number of Music Releases",
  marker = list(color = "#2E3192"),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Released Date: ", release_date,
    "<br>Count: ", music_count
    )
  ) %>%
  add_trace(
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = "Cumulative Count",
    line = list(color = "black", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Released Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Yearly Music (Song/Album) Releases",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )
Show Code
# Data Preparation

# Step 1: Count number of notable music by release date
notable_music_by_date_2 <- mc1_nodes_clean %>%
  filter(name %in% unique(creator_songs_2), notable == TRUE) %>%
  count(release_date, name = "music_count") %>%
  arrange(release_date) %>%  # Ensure dates are in chronological order
  mutate(cumulative_count = cumsum(music_count))



# Visualisation

plot_ly(
  data = notable_music_by_date_2,
  x = ~release_date,
  y = ~music_count,
  type = "bar",
  name = "Number of Notable Music Releases",
  marker = list(color = "#2E3192"),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Released Date: ", release_date,
    "<br>Count: ", music_count
    )
  ) %>%
  add_trace(
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = "Cumulative Count",
    line = list(color = "black", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Released Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Yearly Notable Music (Song/Album) Releases",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )
Show Code
# Data Preparation

# Step 1: Count number of influenced artists by release date
influence_artists_by_date_2 <- creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_from %in% unique(chosen_node_2),
         influence_creator != unique(chosen_node_2)) %>%
  # Get unique artist-date pairs first
  distinct(influence_creator, influence_release_date) %>%
  # Find first influence date for each artist
  group_by(influence_creator) %>%
  summarize(
    first_influence_date = if(n() > 0) min(influence_release_date) else NA_real_,
    .groups = "drop"
  ) %>%
  # Count new artists by first influence date
  count(first_influence_date, name = "music_count") %>%
  arrange(first_influence_date) %>%
  rename(creator_release_date = first_influence_date) %>%
  # Calculate cumulative unique artists
  mutate(cumulative_count = cumsum(music_count))



# Visualisation

plot_ly(
  data = influence_artists_by_date_2,
  x = ~creator_release_date,
  y = ~music_count,
  type = "bar",
  name = "Number of Influenced Artists",
  marker = list(color = "#2E3192"),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Influence Date: ", creator_release_date,
    "<br>Count: ", music_count
    )
  ) %>%
  add_trace(
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = "Cumulative Count",
    line = list(color = "black", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Influence Date: ", creator_release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Annual Count of New Artist Influences & Collaborations",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )
Show Code
# Data Preparation

# Step 1: Count number of influenced music by release date
influence_song_by_date_2 <- mc1_nodes_clean %>%
  filter(name %in% unique(creators_songs_influence_2)) %>%
  count(release_date, name = "music_count") %>%
  arrange(release_date) %>%  # Ensure dates are in chronological order
  mutate(cumulative_count = cumsum(music_count))



# Visualisation

plot_ly(
  data = influence_song_by_date_2,
  x = ~release_date,
  y = ~music_count,
  type = "bar",
  name = "Number of Influenced Music",
  marker = list(color = "#2E3192"),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Influence Date: ", release_date,
    "<br>Count: ", music_count
    )
  ) %>%
  add_trace(
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = "Cumulative Count",
    line = list(color = "black", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Influence Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Yearly Music (Song/Album) Influence",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )

4.2.3 Visualizing the career of Min Fu

Min Fu is the most influential artist.

Show Code
# Data Preparation

chosen_creator_3 = "Min Fu"

# Step 1: Get the node of the chosen creator
chosen_node_3 <- creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_3) %>%
  pull(creator_from) %>%
  unique()

# Step 2: Get the songs that the top creator produced
creator_songs_3 <- creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_3) %>%
  pull(song_to)

# Step 3: Get the songs they have influenced / artists they collaborate with
creators_songs_collaborate_influence_3 <- creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_3,
         infuence_music_collaborate != chosen_node_3) %>%
  pull(infuence_music_collaborate)

# Step 4: Get the songs they have influenced
creators_songs_influence_3 <- creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_3,
         infuence_music_collaborate != chosen_node_3,
         `Edge Colour` == "Influenced By") %>%
  pull(infuence_music_collaborate)

# Step 5: Get the influenced creators of the influenced songs
creators_songs_influence_creators_3 <- creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_3,
         influence_creator != chosen_node_3,
         !is.na(influence_creator),
         infuence_music_collaborate %in% creators_songs_influence_3) %>%
  pull(influence_creator)

all_nodes <- unique(c(chosen_node_3, 
                      creator_songs_3, 
                      creators_songs_collaborate_influence_3,
                      creators_songs_influence_creators_3))

# Create subgraph
sub_graph <- graph %>%
  filter(name %in% all_nodes)



# Visualisation

g <- sub_graph %>%
  ggraph(layout = "fr") + 
  geom_edge_fan(
    aes(
      edge_colour = `Edge Colour`,
      start_cap = circle(1, 'mm'),
      end_cap = circle(1, 'mm')
      ),
    arrow = arrow(length = unit(1, 'mm')),
    alpha = 0.3
  ) +
  geom_point_interactive(
    aes(
      x = x,
      y = y,
      data_id = name,
      colour = `Node Colour`,
      shape = `Node Type`,
      size = ifelse(node_name == chosen_creator_3, 3, 1),
      tooltip = case_when(
        `Node Type` == "Album" ~ sprintf(
          "%s<br/>%s<br/>Notable: %s<br/>(%s)", node_name, genre, notable, release_date
        ),
        `Node Type` == "Song" ~ sprintf(
          "%s<br/>%s<br/>Notable: %s<br/>(%s)<br/>Single: %s", node_name, genre, notable, release_date, single
        ),
        TRUE ~ sprintf("%s", node_name)
      )
    ),
    show.legend = c(size = FALSE)
  )+ 
  geom_node_text(
    aes(
      label = ifelse(node_name == chosen_creator_3, chosen_creator_3, NA)
    ),
    fontface = "bold",
    size = 2.5,
    colour = 'red',
    show.legend = FALSE
  ) +
  scale_shape_manual(
    name = "Node Type",
    values = c(
      "Album" = 16,
      "MusicalGroup" = 15,
      "Person" = 17,
      "Song" = 10
    )
  ) +
  scale_edge_colour_manual(
    name = "Edge Colour",
    values = c(
      `Creator Of` = "#47D45A",
      `Influenced By` = "#FF5757",
      `Member Of` = "#CF57FF"
    )
  ) +
  scale_colour_manual(
    name = "Node Colour",
    values = c(
      "Musician" = "grey50",
      "Oceanus Folk" = "#0027EA",
      "Other Genre" = "#A45200"
    )
  ) +
  theme_graph() +
  theme(legend.text = element_text(size = 6),
        legend.title = element_text(size = 9)) +
  scale_size_identity()

girafe(ggobj = g, width_svg = 7, height_svg = 6)
Show Code
# Data Preparation

# Step 1: Count number of music by release date
music_by_date_3 <- mc1_nodes_clean %>%
  filter(name %in% unique(creator_songs_3)) %>%
  count(release_date, name = "music_count") %>%
  arrange(release_date) %>%  # Ensure dates are in chronological order
  mutate(cumulative_count = cumsum(music_count))



# Visualisation

plot_ly(
  data = music_by_date_3,
  x = ~release_date,
  y = ~music_count,
  type = "bar",
  name = "Number of Music Releases",
  marker = list(color = "#2E3192"),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Released Date: ", release_date,
    "<br>Count: ", music_count
    )
  ) %>%
  add_trace(
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = "Cumulative Count",
    line = list(color = "black", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Released Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Yearly Music (Song/Album) Releases",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )
Show Code
# Data Preparation

# Step 1: Count number of notable music by release date
notable_music_by_date_3 <- mc1_nodes_clean %>%
  filter(name %in% unique(creator_songs_3), notable == TRUE) %>%
  count(release_date, name = "music_count") %>%
  arrange(release_date) %>%  # Ensure dates are in chronological order
  mutate(cumulative_count = cumsum(music_count))



# Visualisation

plot_ly(
  data = notable_music_by_date_3,
  x = ~release_date,
  y = ~music_count,
  type = "bar",
  name = "Number of Notable Music Releases",
  marker = list(color = "#2E3192"),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Released Date: ", release_date,
    "<br>Count: ", music_count
    )
  ) %>%
  add_trace(
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = "Cumulative Count",
    line = list(color = "black", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Released Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Yearly Notable Music (Song/Album) Releases",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )
Show Code
# Data Preparation

# Step 1: Count number of influenced artists by release date
influence_artists_by_date_3 <- creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_from %in% unique(chosen_node_3),
         influence_creator != unique(chosen_node_3)) %>%
  # Get unique artist-date pairs first
  distinct(influence_creator, influence_release_date) %>%
  # Find first influence date for each artist
  group_by(influence_creator) %>%
  summarize(
    first_influence_date = if(n() > 0) min(influence_release_date) else NA_real_,
    .groups = "drop"
  ) %>%
  # Count new artists by first influence date
  count(first_influence_date, name = "music_count") %>%
  arrange(first_influence_date) %>%
  rename(creator_release_date = first_influence_date) %>%
  # Calculate cumulative unique artists
  mutate(cumulative_count = cumsum(music_count))



# Visualisation

plot_ly(
  data = influence_artists_by_date_3,
  x = ~creator_release_date,
  y = ~music_count,
  type = "bar",
  name = "Number of Influenced Artists",
  marker = list(color = "#2E3192"),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Influence Date: ", creator_release_date,
    "<br>Count: ", music_count
    )
  ) %>%
  add_trace(
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = "Cumulative Count",
    line = list(color = "black", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Influence Date: ", creator_release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Annual Count of New Artist Influences & Collaborations",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )
Show Code
# Data Preparation

# Step 1: Count number of influenced music by release date
influence_song_by_date_3 <- mc1_nodes_clean %>%
  filter(name %in% unique(creators_songs_influence_3)) %>%
  count(release_date, name = "music_count") %>%
  arrange(release_date) %>%  # Ensure dates are in chronological order
  mutate(cumulative_count = cumsum(music_count))



# Visualisation

plot_ly(
  data = influence_song_by_date_3,
  x = ~release_date,
  y = ~music_count,
  type = "bar",
  name = "Number of Influenced Music",
  marker = list(color = "#2E3192"),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Influence Date: ", release_date,
    "<br>Count: ", music_count
    )
  ) %>%
  add_trace(
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = "Cumulative Count",
    line = list(color = "black", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Influence Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Yearly Music (Song/Album) Influence",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )

4.2.4 Compare and contrast the three artists’ rise in popularity and influence

The cumulative graphs of the 3 artists (Sailor Shift, Jay Walters and Min Fu) will be overlaid for comparison of their musical careers.

Show Code
# Visualisation

plot_ly(
  data = music_by_date_1,
  x = ~release_date,
  y = ~cumulative_count,
  type = "scatter",
  mode = "lines+markers",
  name = chosen_creator_1,
  line = list(color = "#2E3192", width = 2),
  marker = list(color = "red", size = 6),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Artist: ", chosen_creator_1,
    "<br>Influence Date: ", release_date,
    "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  add_trace(
    data = music_by_date_2,
    x = ~release_date,
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = chosen_creator_2,
    line = list(color = "green", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Artist: ", chosen_creator_2,
      "<br>Influence Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  add_trace(
    data = music_by_date_3,
    x = ~release_date,
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = chosen_creator_3,
    line = list(color = "purple", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Artist: ", chosen_creator_3,
      "<br>Influence Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Yearly Music (Song/Album) Releases",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )
Show Code
# Visualisation

plot_ly(
  data = notable_music_by_date_1,
  x = ~release_date,
  y = ~cumulative_count,
  type = "scatter",
  mode = "lines+markers",
  name = chosen_creator_1,
  line = list(color = "#2E3192", width = 2),
  marker = list(color = "red", size = 6),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Artist: ", chosen_creator_1,
    "<br>Influence Date: ", release_date,
    "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  add_trace(
    data = notable_music_by_date_2,
    x = ~release_date,
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = chosen_creator_2,
    line = list(color = "green", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Artist: ", chosen_creator_2,
      "<br>Influence Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  add_trace(
    data = notable_music_by_date_3,
    x = ~release_date,
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = chosen_creator_3,
    line = list(color = "purple", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Artist: ", chosen_creator_3,
      "<br>Influence Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Yearly Notable Music (Song/Album) Releases",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )
Show Code
# Visualisation

plot_ly(
  data = influence_artists_by_date_1,
  x = ~creator_release_date,
  y = ~cumulative_count,
  type = "scatter",
  mode = "lines+markers",
  name = chosen_creator_1,
  line = list(color = "#2E3192", width = 2),
  marker = list(color = "red", size = 6),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Artist: ", chosen_creator_1,
    "<br>Influence Date: ", creator_release_date,
    "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  add_trace(
    data = influence_artists_by_date_2,
    x = ~creator_release_date,
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = chosen_creator_2,
    line = list(color = "green", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Artist: ", chosen_creator_2,
      "<br>Influence Date: ", creator_release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  add_trace(
    data = influence_artists_by_date_3,
    x = ~creator_release_date,
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = chosen_creator_3,
    line = list(color = "purple", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Artist: ", chosen_creator_3,
      "<br>Influence Date: ", creator_release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Annual Count of New Artist Influences & Collaborations",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )
Note

Sailor Shift has only Collaborations and no Artist Influence

Show Code
# Visualisation

plot_ly(
  data = influence_song_by_date_1,
  x = ~release_date,
  y = ~cumulative_count,
  type = "scatter",
  mode = "lines+markers",
  name = chosen_creator_1,
  line = list(color = "#2E3192", width = 2),
  marker = list(color = "red", size = 6),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Artist: ", chosen_creator_1,
    "<br>Influence Date: ", release_date,
    "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  add_trace(
    data = influence_song_by_date_2,
    x = ~release_date,
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = chosen_creator_2,
    line = list(color = "green", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Artist: ", chosen_creator_2,
      "<br>Influence Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  add_trace(
    data = influence_song_by_date_3,
    x = ~release_date,
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = chosen_creator_3,
    line = list(color = "purple", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Artist: ", chosen_creator_3,
      "<br>Influence Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Yearly Music (Song/Album) Influence",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )
Note

Sailor Shift has no influence on music creation

Visualize the careers of three artists. Compare and contrast their rise in popularity and influence?
  • Among the three artists, Sailor Shift is the most recent entrant to the music industry, having debuted in 2024.
    • She maintained consistent music releases through 2040.
    • With 38 total releases, she surpassed Jay Walters’ record (37 releases) in 2040, establishing herself as the most prolific artist.
    • Min Fu has 12 records.
  • While Sailor Shift has released more music than Jay Walters, Jay Walters is the most popular Artist having released 35 hits that made the top record chart.
    • Sailor Shift has 25 hits while Min Fu has 12.
  • Min Fu is the most influential artist having influenced/collaborated with 598 artists and influenced the creation of 192 music.
    • Jay Walters has a much lower influence having influence/collaborated with 51 artists and 50 music.
    • Sailor Shift has a similar influence of 48 artists (all collaborations) but 0 influence on the music scene.
  • However, Sailor Shift has the most recent influence, with new artist connections as recent as 2040.
    • In contrast, Jay Walters’ influence period ended earlier (last connection in 2035), while Min Fu’s influence dates back to 2030.

4.3 Part 3b) Using this characterization, give three predictions of who the next Oceanus Folk stars with be over the next five years.

This will be a continuation of Part 3a where only Oceanus Folk artists and the Oceanus Folk songs that they have produced are analysed.

4.3.1 Analysing the careers of all Oceanus Folk Artists

In order to predict the next Oceanus Folk stars, a grading rubric will be used where their ranking in these 4 categories (Number of Music released, Number of Notable Music, Number of New Artists Influenced / Collaborated With and Number of Music influenced). Each category will be given equal weighting with a maximum score of 4 and the Oceanus Folk artists will be ranked using this rubric.

Show Code
genre_creators = creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(song_genre == "Oceanus Folk") %>%
  pull(creator_from)

# Step 1: Filter to only Oceanus Folk Artists
oceanus_creator_and_songs_and_influences_and_creators_collaborate <- creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_from %in% genre_creators, song_genre == "Oceanus Folk")

# Step 2: To highlight songs that the creator influence that is not produced by same creator
oceanus_creator_influence_lists <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
  group_by(creator_name, creator_node_type, song_to, song_name, creator_release_date, song_genre, notable) %>%
  distinct () %>%
  summarize(
    unique_collaborate = list(unique(na.omit(infuence_music_collaborate[creator_from != influence_creator & `Edge Colour` == "Creator Of"]))),
    unique_influence_creators = list(unique(na.omit(influence_creator[creator_from != influence_creator & `Edge Colour` == "Influenced By"]))),
    unique_influence_music = list(unique(na.omit(infuence_music_collaborate[creator_from != influence_creator & !is.na(influence_genre)])))
  )

# Step 3: Aggregate unique influences per creator
oceanus_creator_stats <- oceanus_creator_influence_lists %>%
  group_by(creator_name) %>%
  summarize(
    total_songs = n_distinct(song_to),
    notable_hits = sum(notable == TRUE, na.rm = TRUE),
    collaboration = length(unique(unlist(unique_collaborate))),
    influence_creators = length(unique(unlist(unique_influence_creators))),
    collaboration_influence_creator = length(unique(c(unlist(unique_influence_creators),unlist( unique_collaborate)))),
    influence_music = length(unique(unlist(unique_influence_music)))
  )

# Step 4: Create Scoring Rubric
oceanus_creator_rankings <- oceanus_creator_stats %>%
  mutate(
    # Normalize each metric (0-1 scale)
    songs_score = (total_songs - min(total_songs)) / (max(total_songs) - min(total_songs)),
    notable_score = (notable_hits - min(notable_hits)) / (max(notable_hits) - min(notable_hits)),
    artists_score = (collaboration_influence_creator - min(collaboration_influence_creator)) / (max(collaboration_influence_creator) - min(collaboration_influence_creator)),
    music_score = (influence_music - min(influence_music)) / (max(influence_music) - min(influence_music)),
    
    # Calculate composite score (equal weighting)
    composite_score = songs_score + notable_score + artists_score + music_score
  ) %>%
  arrange(desc(composite_score)) %>%
  select(creator_name, total_songs, notable_hits, collaboration_influence_creator, influence_music, 
         composite_score)

# Final Ranked Table
oceanus_creator_rankings %>%
  mutate(
    Rank = row_number(),
    `Star Factor` = round(composite_score, 3)
  ) %>%
  select(Rank, creator_name, total_songs, notable_hits, collaboration_influence_creator, 
         influence_music, `Star Factor`) %>%
  rename(
    `Artist` = creator_name,
    `Total Music` = total_songs,
    `Notable Hits` = notable_hits,
    `Artist Influ & Colab` = collaboration_influence_creator,
    `Music Influenced` = influence_music
  ) %>%
  kable(caption = "Oceanus Folk Artists Ranked by Star Factor") %>%
  kable_styling("striped", full_width = F) %>%
  scroll_box(height = "300px")
Oceanus Folk Artists Ranked by Star Factor
Rank Artist Total Music Notable Hits Artist Influ & Colab Music Influenced Star Factor
1 Sailor Shift 36 24 41 0 2.216
2 Chao Wu 4 3 190 44 2.211
3 Xia Jia 4 3 178 42 2.102
4 Donna Caldwell 2 1 170 41 1.897
5 Xiulan Ye 2 1 170 41 1.897
6 Sandra Smith 1 0 162 39 1.739
7 Zacharie Martins 4 4 136 32 1.695
8 Constance Guibert 3 3 117 28 1.434
9 Ignazio Pastine 3 3 117 28 1.434
10 Embers of Wrath 1 1 107 26 1.196
11 Ann Holland 1 1 99 23 1.085
12 Eric Beasley 1 1 99 23 1.085
13 Jun Han 2 2 98 20 1.082
14 Chao Tan 4 4 85 15 1.041
15 Filippo Pelli 5 4 77 15 1.027
16 Lei Wan 1 1 95 20 0.996
17 Tao Ren 1 1 95 20 0.996
18 Yesenia Miller 1 1 95 20 0.996
19 Lei Tan 2 2 76 13 0.807
20 Isabella Farinelli 3 2 65 13 0.778
21 Yang Yao 3 2 63 12 0.745
22 Gang Tao 1 1 71 13 0.711
23 Li Qiu 1 1 71 13 0.711
24 Min He 1 1 71 13 0.711
25 Ping Yan 1 1 71 13 0.711
26 Wei Ma 1 1 71 13 0.711
27 Na Ren 3 3 50 10 0.673
28 Alfred Thibault 3 3 35 11 0.616
29 Synaptic Stream 3 3 35 11 0.616
30 Zoé-Agnès Delaunay 3 3 35 11 0.616
31 Wei Gao 2 2 48 11 0.615
32 Chao Qiao 2 2 52 10 0.613
33 Jun Xu 2 2 52 10 0.613
34 Vanessa Ramos 2 2 47 9 0.564
35 Lei Zeng 2 1 47 10 0.545
36 Drowned Harbor 12 5 4 0 0.544
37 Ivy Echos 11 4 13 1 0.544
38 Orla Seabloom 8 8 0 0 0.533
39 Adriana Figueroa 1 1 49 10 0.527
40 Charles Hall 1 1 49 10 0.527
41 Fang Wu 1 1 49 10 0.527
42 James Mosley 1 1 49 10 0.527
43 Thomas Dudley 1 1 49 10 0.527
44 Tao Yao 3 3 43 5 0.522
45 Juan Yu 3 3 34 7 0.520
46 Lei Jin 3 3 34 7 0.520
47 Yong Lai 3 3 34 7 0.520
48 Yong Shen 3 3 34 7 0.520
49 Guiying Dai 1 1 46 10 0.511
50 Yong Chen 1 1 46 10 0.511
51 Juan Duan 1 1 46 9 0.488
52 Tao Ye 1 1 46 9 0.488
53 Yong Guo 1 1 46 9 0.488
54 Lei Shen 2 2 36 8 0.483
55 Xia Cui 2 2 36 8 0.483
56 Xiuying Li 2 2 36 8 0.483
57 Qiang Song 3 3 35 5 0.480
58 Beatrice Albright 7 7 3 0 0.479
59 Daniel O'Connell 7 7 3 0 0.479
60 Copper Canyon Ghosts 7 7 2 0 0.474
61 Jie Cui 3 3 25 6 0.450
62 Tao Jin 4 4 24 3 0.447
63 Yong Dong 4 4 24 3 0.447
64 The Tide Singer's Knot 8 4 6 1 0.421
65 The Saltwater Weavers 9 4 4 0 0.416
66 Min Jin 2 2 27 7 0.413
67 Ming Chen 1 1 36 8 0.413
68 Lisa Martin 2 2 30 6 0.406
69 Monica Nelson 2 2 30 6 0.406
70 Guglielmo Canetta 3 3 16 6 0.403
71 Nicola Bernetti 3 3 16 6 0.403
72 Serena Lettiere 3 3 16 6 0.403
73 Vincentio Gentili 3 3 16 6 0.403
74 Sophie Ramirez 5 5 15 0 0.402
75 Maya Jensen 5 5 12 0 0.386
76 Georgina Piñol-Guerra 1 0 43 7 0.385
77 Lalo Salgado Cazorla 1 0 43 7 0.385
78 Luigina Stoppani-Sandi 1 0 43 7 0.385
79 Tao Lei 2 2 30 5 0.383
80 The Brine Choir 7 4 6 0 0.370
81 Aneta Pruschke-Sölzer 2 2 23 6 0.369
82 Cesare Cicilia 2 2 23 6 0.369
83 Courtney Phillips 1 1 32 7 0.369
84 Jennifer Diaz 1 1 32 7 0.369
85 Kelli Turner 1 1 32 7 0.369
86 Latasha Chavez 1 1 32 7 0.369
87 Min Qian 1 1 32 7 0.369
88 Na Wen 3 3 21 3 0.361
89 Thibault Hardy 2 2 21 6 0.359
90 Coralia Bellweather 5 5 6 0 0.354
91 Bryan Smith 1 1 33 6 0.352
92 Emily Jackson 1 1 33 6 0.352
93 Gregory Williams 1 1 33 6 0.352
94 Ronald Kennedy 1 1 33 6 0.352
95 Adèle Bonneau 2 2 19 6 0.348
96 James Clark 1 1 30 6 0.336
97 Kristin Anderson 1 1 30 6 0.336
98 Ming Su 1 1 30 6 0.336
99 Guiying Liao 2 2 18 5 0.320
100 Ping Sun 2 2 18 5 0.320
101 Yan Zou 2 2 18 5 0.320
102 Jie Dai 1 1 27 6 0.320
103 Juan Gu 1 1 27 6 0.320
104 Ping Pan 1 1 27 6 0.320
105 Qiang Dai 1 1 27 6 0.320
106 Tao Chang 1 1 27 6 0.320
107 Xiulan He 1 1 27 6 0.320
108 Na Yang 2 2 25 3 0.312
109 Lauretta Tresoldi 2 2 20 4 0.308
110 Xiulan Xu 2 2 17 4 0.292
111 Wei Guo 2 2 21 3 0.291
112 Levi Holloway 4 4 7 0 0.289
113 Jie Li 2 2 20 3 0.285
114 Tao Dai 2 2 20 3 0.285
115 Jonah Calloway 4 4 6 0 0.284
116 Marin Thorne 4 4 6 0 0.284
117 Alphons Donati-Pederiva 2 2 19 3 0.280
118 Augusto Villaverde Armengol 2 2 19 3 0.280
119 Feliciano Pizarro Moll 2 2 19 3 0.280
120 Juan Dong 2 2 19 3 0.280
121 Jing Long 1 1 28 4 0.280
122 Tao Qin 1 1 28 4 0.280
123 Xiuying Xiang 1 1 28 4 0.280
124 Yan Feng 1 1 28 4 0.280
125 Barnaby Quill 4 4 4 0 0.273
126 Jonas Fenwick 4 4 4 0 0.273
127 Marina Grey 4 4 4 0 0.273
128 Orin Caldwell 4 4 4 0 0.273
129 Selena Marrow 4 4 4 0 0.273
130 Silas Finch 4 4 4 0 0.273
131 Thorne Rivenhall 4 4 4 0 0.273
132 Brandy Reyes 1 1 26 4 0.269
133 Juan Qiao 1 1 26 4 0.269
134 Jun Jin 1 1 26 4 0.269
135 Laura Gibbs 1 1 26 4 0.269
136 Mary Medina 1 1 26 4 0.269
137 Na Ma 1 1 26 4 0.269
138 Raymond Mccoy 1 1 26 4 0.269
139 Rhonda Brown 1 1 26 4 0.269
140 Xiulan Ren 1 1 26 4 0.269
141 Na Peng 2 1 29 2 0.268
142 Gang Zhu 2 2 21 2 0.268
143 Ming Yu 2 2 21 2 0.268
144 Jing Zhong 2 2 15 3 0.259
145 Albert Miller 2 2 19 2 0.257
146 Adrien Weiss 1 1 23 4 0.254
147 Charles du Perez 1 1 23 4 0.254
148 Pauline Guyot de la Maillet 1 1 23 4 0.254
149 Qiang Qian 2 2 18 2 0.252
150 Walter White 2 2 18 2 0.252
151 Xia Xia 2 2 18 2 0.252
152 Édouard-Rémy Clerc 1 1 18 5 0.250
153 Qiang Xie 2 2 17 2 0.247
154 Xiuying Huang 2 2 17 2 0.247
155 Chao Xiao 2 2 16 2 0.242
156 Laetitia Petitjean 2 2 11 3 0.238
157 Madeleine Pineau 2 2 11 3 0.238
158 Susan Da Silva 2 2 11 3 0.238
159 Émilie Maillet 2 2 11 3 0.238
160 Antonietta Zamengo-Zanazzo 1 1 20 4 0.238
161 Patrizio Antonello-Salvemini 1 1 20 4 0.238
162 Xiulan Mo 3 3 9 0 0.230
163 Tidal Reverie 5 2 6 0 0.229
164 Jade Thompson 3 3 8 0 0.224
165 Lila "Lilly" Hartman 3 3 8 0 0.224
166 Ping Peng 2 2 12 2 0.221
167 Ethan Sanchez 1 1 21 3 0.220
168 Fang Gu 1 1 21 3 0.220
169 Ping Cui 1 1 21 3 0.220
170 Xiuying Yin 1 1 21 3 0.220
171 Yan Fang 1 1 21 3 0.220
172 Yan Liang 1 1 21 3 0.220
173 Selkie's Hollow 5 2 4 0 0.219
174 Wei Qian 2 2 11 2 0.215
175 Chao Tang 1 1 20 3 0.215
176 Gang Yin 1 1 20 3 0.215
177 Gang Zhang 1 1 20 3 0.215
178 Guiying Ding 1 1 20 3 0.215
179 Li Zou 1 1 20 3 0.215
180 Na Zou 1 1 20 3 0.215
181 Wei Chang 1 1 20 3 0.215
182 Xiuying Du 1 1 20 3 0.215
183 Arlo Sterling 3 3 6 0 0.214
184 Cassian Rae 3 3 6 0 0.214
185 Elara May 3 3 6 0 0.214
186 Lyra Blaze 3 3 6 0 0.214
187 Orion Cruz 3 3 6 0 0.214
188 Yan Zhou 2 2 15 1 0.214
189 Coriolano Luria-Scialpi 1 1 15 4 0.212
190 Arnaude Poirier 1 1 19 3 0.210
191 Christophe-Émile Grégoire 1 1 19 3 0.210
192 Georges Chrétien 1 1 19 3 0.210
193 Interval Shift 1 1 19 3 0.210
194 Luce Loiseau 1 1 19 3 0.210
195 Martino Michelangeli 1 1 19 3 0.210
196 Caleb Foster 3 3 5 0 0.208
197 Eleanor Wren 3 3 5 0 0.208
198 Kai Reynolds 3 3 5 0 0.208
199 Jie Xu 2 2 13 1 0.203
200 Na Dai 2 2 13 1 0.203
201 Xia Wu 2 2 13 1 0.203
202 Christine Ward 1 1 17 3 0.199
203 Donald Medina 1 1 17 3 0.199
204 Erik Mathews 1 1 17 3 0.199
205 Gang Shao 1 1 17 3 0.199
206 Jie Fan 1 1 17 3 0.199
207 Joshua Orozco 1 1 17 3 0.199
208 Jun Pan 1 1 17 3 0.199
209 Lisa Hofmann 1 1 17 3 0.199
210 Min Liang 1 1 17 3 0.199
211 Na Li 1 1 17 3 0.199
212 Na Yi 1 1 17 3 0.199
213 Sonora Flux 1 1 17 3 0.199
214 Steven Goodman 1 1 17 3 0.199
215 Wei Liao 1 1 17 3 0.199
216 Xia Shao 1 1 17 3 0.199
217 Xia Wang 1 1 17 3 0.199
218 Xiulan Lin 1 1 17 3 0.199
219 Yang Wu 1 1 17 3 0.199
220 The Wave Riders 4 2 5 0 0.195
221 Michael Reyes 2 2 7 2 0.194
222 Michael Rivera 2 2 7 2 0.194
223 Eric Hopkins 1 1 16 3 0.194
224 Guiying Tao 1 1 16 3 0.194
225 Min Bai 1 1 16 3 0.194
226 Ming Qiao 1 1 16 3 0.194
227 Ryan Mcguire 1 1 16 3 0.194
228 Stephen Allen 1 1 16 3 0.194
229 Yang Dai 1 1 16 3 0.194
230 Jun Yuan 2 2 11 1 0.193
231 Jing Xu 1 1 15 3 0.189
232 Juan Wen 1 1 15 3 0.189
233 Min Xue 1 1 15 3 0.189
234 Min Yu 1 1 15 3 0.189
235 Loro 2 2 10 1 0.187
236 Xiulan Yi 2 2 10 1 0.187
237 Siren's Call 4 2 3 0 0.185
238 Xiulan Dong 2 2 9 1 0.182
239 Yong Tang 2 2 9 1 0.182
240 Mariner's Refrain 4 1 6 1 0.182
241 Rüdiger Graf 2 1 12 2 0.179
242 Na Zhong 2 2 8 1 0.177
243 Evelyn Gordon 1 1 17 2 0.177
244 Gang Wang 1 1 17 2 0.177
245 Jie Yan 1 1 17 2 0.177
246 Min Gao 1 1 17 2 0.177
247 Richard Frazier 1 1 17 2 0.177
248 Sonia Weeks 1 1 17 2 0.177
249 Wei Cheng 1 1 17 2 0.177
250 Yan Hou 1 1 17 2 0.177
251 Lei Tao 1 0 24 2 0.172
252 Ming Song 1 0 24 2 0.172
253 Nadia van Dooren-de Jode Vastraedsd 1 0 24 2 0.172
254 Tao Zhao 1 0 24 2 0.172
255 Eric Bartlett 1 1 16 2 0.171
256 Min Yan 1 1 16 2 0.171
257 Xia Zeng 1 1 16 2 0.171
258 Yong Tao 2 2 11 0 0.170
259 Joker's Smile 1 1 7 4 0.169
260 Genevieve Bell 4 2 0 0 0.169
261 Chao Zeng 1 1 11 3 0.168
262 Gang Jiang 1 1 11 3 0.168
263 Xia Xiang 1 1 11 3 0.168
264 Guiying Ma 1 1 15 2 0.166
265 Jie Wu 1 1 15 2 0.166
266 Jing Li 1 1 15 2 0.166
267 Na Qian 1 1 15 2 0.166
268 Yong Wen 1 1 15 2 0.166
269 Jun Yi 1 1 14 2 0.161
270 Xia Yu 1 1 14 2 0.161
271 Min Huang 2 2 9 0 0.159
272 Yang Ma 2 2 9 0 0.159
273 David Nash 1 1 13 2 0.156
274 Fang Duan 1 1 13 2 0.156
275 Gang Yang 1 1 13 2 0.156
276 Jessica Beck 1 1 13 2 0.156
277 Kaori Kobayashi 1 1 13 2 0.156
278 Wei Lai 1 1 13 2 0.156
279 Xia Liu 1 1 13 2 0.156
280 Yong Dai 1 1 13 2 0.156
281 Yui Kondo 1 1 13 2 0.156
282 Juan Wu 2 2 8 0 0.154
283 Rusty Riggins 2 2 8 0 0.154
284 Ella Parker 3 1 6 1 0.153
285 Guiying Cao 1 1 12 2 0.150
286 Jing Kang 1 1 12 2 0.150
287 Luciano Chindamo 1 1 12 2 0.150
288 Silver Seraph 1 1 12 2 0.150
289 Chao Qiu 2 2 7 0 0.149
290 Yoko Fujita 2 2 7 0 0.149
291 Chao Zhou 1 1 11 2 0.145
292 Fenna Zwart 1 1 11 2 0.145
293 Kevin Ruiz 1 1 11 2 0.145
294 Kimberly Stanton 1 1 11 2 0.145
295 Lei Wei 1 1 11 2 0.145
296 Michael Anderson 1 1 11 2 0.145
297 Xiulan Gu 1 1 11 2 0.145
298 Ethan Clarke 2 2 6 0 0.143
299 Kara Lee 2 2 6 0 0.143
300 Michael Harris 2 2 6 0 0.143
301 Sophie Bennett 2 2 6 0 0.143
302 Tao Cui 2 2 6 0 0.143
303 Jose Garrett 1 1 10 2 0.140
304 Juan Pan 1 1 10 2 0.140
305 Juan Zeng 1 1 10 2 0.140
306 Lupe Miró 1 1 10 2 0.140
307 Ming Wan 1 1 10 2 0.140
308 Paulo Oestrovsky 1 1 10 2 0.140
309 Qiang He 1 1 10 2 0.140
310 Qiang Lai 1 1 10 2 0.140
311 Qiang Zhu 1 1 10 2 0.140
312 Aiden Harper 2 2 5 0 0.138
313 Finn Morgan 2 2 5 0 0.138
314 Skylar Brooks 2 2 5 0 0.138
315 Christine Turner 1 1 9 2 0.134
316 Gang Zeng 1 1 9 2 0.134
317 Li Dai 1 1 9 2 0.134
318 Min Ding 1 1 9 2 0.134
319 Nicholas Porter 1 1 9 2 0.134
320 Victoria Higgins 1 1 9 2 0.134
321 Xiuying Jia 1 1 9 2 0.134
322 Jing Guo 1 1 13 1 0.133
323 Li Wan 1 1 13 1 0.133
324 Mandy Hood 1 1 13 1 0.133
325 Min Kong 1 1 13 1 0.133
326 Operatic Pulse 1 1 13 1 0.133
327 Robert Villanueva 1 1 13 1 0.133
328 Yang Zhao 1 1 13 1 0.133
329 Carlos Duffy 2 2 3 0 0.128
330 Ewan MacCrae 2 2 3 0 0.128
331 Freya Lindholm 2 2 3 0 0.128
332 Justin Berry 2 2 3 0 0.128
333 Lila Rivers 2 2 3 0 0.128
334 Mia Waters 2 2 3 0 0.128
335 Mikayla Cook 2 2 3 0 0.128
336 Min Qin 2 2 3 0 0.128
337 Ming Du 1 1 12 1 0.128
338 Morgan Hernandez 1 1 12 1 0.128
339 Patricia Pope 1 1 12 1 0.128
340 Ping Shao 1 1 12 1 0.128
341 Yang Liang 1 1 12 1 0.128
342 Gang Fu 1 1 6 2 0.119
343 Li Long 1 1 6 2 0.119
344 Ping Zeng 1 1 6 2 0.119
345 Chao Fang 1 1 10 1 0.117
346 Gang Ding 1 1 10 1 0.117
347 Guiying Jin 1 1 10 1 0.117
348 Min Lei 1 1 10 1 0.117
349 Nicholas Moss 1 1 10 1 0.117
350 Qiang Jiang 1 1 10 1 0.117
351 Rachel Rios 1 1 10 1 0.117
352 Tammie Johnson 1 1 10 1 0.117
353 Xia Shen 1 1 10 1 0.117
354 Xiulan Zhou 1 1 10 1 0.117
355 Yan Chang 1 1 10 1 0.117
356 Yang Zhang 1 1 10 1 0.117
357 Beppe Agazzi-Muratori 1 1 9 1 0.112
358 Chao Jin 1 1 9 1 0.112
359 Chiyo Suzuki 1 1 9 1 0.112
360 Elena Nibali 1 1 9 1 0.112
361 Gang Bai 1 1 9 1 0.112
362 Jeffery Bailey 1 1 9 1 0.112
363 Jing Wei 1 1 9 1 0.112
364 Kana Mori 1 1 9 1 0.112
365 Kaori Watanabe 1 1 9 1 0.112
366 Karen Robinson 1 1 9 1 0.112
367 Li Yang 1 1 9 1 0.112
368 Min Zheng 1 1 9 1 0.112
369 Ming Wang 1 1 9 1 0.112
370 Ming Xiang 1 1 9 1 0.112
371 Ming Yan 1 1 9 1 0.112
372 Na Deng 1 1 9 1 0.112
373 Renata Manolesso 1 1 9 1 0.112
374 Taichi Sasaki 1 1 9 1 0.112
375 Tao Hu 1 1 9 1 0.112
376 Tao Zhang 1 1 9 1 0.112
377 Tomoya Nakamura 1 1 9 1 0.112
378 Tomoya Takahashi 1 1 9 1 0.112
379 Wei Chen 1 1 9 1 0.112
380 Xiulan Wang 1 1 9 1 0.112
381 Xiuying Kang 1 1 9 1 0.112
382 Yan Luo 1 1 9 1 0.112
383 Yuki Hashimoto 1 1 9 1 0.112
384 Zaira Balotelli-Peano 1 1 9 1 0.112
385 Eduardo Gonzalez 1 1 8 1 0.106
386 Guiying Qin 1 1 8 1 0.106
387 Justin Morse 1 1 8 1 0.106
388 Li He 1 1 8 1 0.106
389 Na Jin 1 1 8 1 0.106
390 Theresa Rivera 1 1 8 1 0.106
391 William Lynch 1 1 8 1 0.106
392 Amanda Ramos 1 1 7 1 0.101
393 Deborah Davis 1 1 7 1 0.101
394 Jie Chang 1 1 7 1 0.101
395 Jie Wan 1 1 7 1 0.101
396 Jie Wang 1 1 7 1 0.101
397 Kent Chen 1 1 7 1 0.101
398 Lei Jia 1 1 7 1 0.101
399 Li Gu 1 1 7 1 0.101
400 Min Liao 1 1 7 1 0.101
401 Min Zou 1 1 7 1 0.101
402 Regina Davis 1 1 7 1 0.101
403 Russell Bates 1 1 7 1 0.101
404 Tao Qian 1 1 7 1 0.101
405 Tao Su 1 1 7 1 0.101
406 Teresa Rivera 1 1 7 1 0.101
407 Whitney Humphrey 1 1 7 1 0.101
408 Xia Du 1 1 7 1 0.101
409 Xiuying Deng 1 1 7 1 0.101
410 Xiuying Fan 1 1 7 1 0.101
411 Xiuying Zhang 1 1 7 1 0.101
412 Yang Wan 1 1 7 1 0.101
413 Alexej Klotz 1 1 6 1 0.096
414 Autumn Walker 1 1 6 1 0.096
415 Christopher Smith 1 1 6 1 0.096
416 Elsbeth Löwer 1 1 6 1 0.096
417 Gang Meng 1 1 6 1 0.096
418 Guiying Pan 1 1 6 1 0.096
419 Jing Ding 1 1 6 1 0.096
420 Jing Zhang 1 1 6 1 0.096
421 Lagan Bhalla 1 1 6 1 0.096
422 Leif Bien 1 1 6 1 0.096
423 Li Xie 1 1 6 1 0.096
424 Li Zeng 1 1 6 1 0.096
425 Margaretha Sager 1 1 6 1 0.096
426 Nakul Samra 1 1 6 1 0.096
427 Ping Zheng 1 1 6 1 0.096
428 Timothy Hansen 1 1 6 1 0.096
429 Xia Zhu 1 1 6 1 0.096
430 Xiuying Duan 1 1 6 1 0.096
431 Jing Luo 1 1 5 1 0.091
432 Lei Qiu 1 1 5 1 0.091
433 Matthew Murphy 1 1 5 1 0.091
434 Qiang Cui 1 1 5 1 0.091
435 Xiulan Zhong 1 1 5 1 0.091
436 Ann Anderson 1 1 4 1 0.085
437 Min Mo 1 1 4 1 0.085
438 Ming Zhong 1 1 4 1 0.085
439 Ping Liao 1 1 4 1 0.085
440 Xiuying Mo 1 1 4 1 0.085
441 Finn McGraw 1 1 8 0 0.084
442 William Tidewell 1 1 8 0 0.084
443 Fang Yan 1 1 7 0 0.079
444 Jie Dong 1 1 7 0 0.079
445 Jie Long 1 1 7 0 0.079
446 Jing Chang 1 1 7 0 0.079
447 Juan Cao 1 1 7 0 0.079
448 Juan Xiang 1 1 7 0 0.079
449 Jun Lai 1 1 7 0 0.079
450 Li Dong 1 1 7 0 0.079
451 Min Guo 1 1 7 0 0.079
452 Qiang Gao 1 1 7 0 0.079
453 Qiang Sun 1 1 7 0 0.079
454 Tao Xu 1 1 7 0 0.079
455 Wei Yao 1 1 7 0 0.079
456 Yong Qiao 1 1 7 0 0.079
457 Leo Hamilton 2 0 5 1 0.078
458 Noah Bennett 2 0 5 1 0.078
459 Min Song 1 1 2 1 0.075
460 Yong Xia 1 1 2 1 0.075
461 Christopher Lowery 1 1 6 0 0.073
462 Ela Batra 1 1 6 0 0.073
463 Guiying Dong 1 1 6 0 0.073
464 Guiying Tang 1 1 6 0 0.073
465 Guiying Xia 1 1 6 0 0.073
466 Guiying Zhong 1 1 6 0 0.073
467 Jun Qiao 1 1 6 0 0.073
468 Jun Shao 1 1 6 0 0.073
469 Kimaya Srinivasan 1 1 6 0 0.073
470 Lei Liao 1 1 6 0 0.073
471 Lei Qian 1 1 6 0 0.073
472 Li Ding 1 1 6 0 0.073
473 Melissa Wheeler 1 1 6 0 0.073
474 Ming Yao 1 1 6 0 0.073
475 Na Cai 1 1 6 0 0.073
476 Na Guo 1 1 6 0 0.073
477 Na Jiang 1 1 6 0 0.073
478 Onkar Kata 1 1 6 0 0.073
479 Qiang Du 1 1 6 0 0.073
480 Qiang Yang 1 1 6 0 0.073
481 Ryan Devan 1 1 6 0 0.073
482 Tami Cox 1 1 6 0 0.073
483 Tao Long 1 1 6 0 0.073
484 Tara Kota 1 1 6 0 0.073
485 Thomas Ball 1 1 6 0 0.073
486 Tiya Sani 1 1 6 0 0.073
487 Wei Bai 1 1 6 0 0.073
488 Xia Kang 1 1 6 0 0.073
489 Yuvraj Dar 1 1 6 0 0.073
490 Amber Smith 1 1 5 0 0.068
491 Cardboard Twenty-Five 1 1 5 0 0.068
492 Chao Kang 1 1 5 0 0.068
493 Chao Wan 1 1 5 0 0.068
494 Chao Zheng 1 1 5 0 0.068
495 Cosmic Drift 1 1 5 0 0.068
496 Crimson Theorem 1 1 5 0 0.068
497 Daria Ceravolo 1 1 5 0 0.068
498 Fang Han 1 1 5 0 0.068
499 Fang Qiao 1 1 5 0 0.068
500 Fang Zhong 1 1 5 0 0.068
501 Fiesta Nocturna 1 1 5 0 0.068
502 Gang Fan 1 1 5 0 0.068
503 Guiying Qiu 1 1 5 0 0.068
504 Guiying Ye 1 1 5 0 0.068
505 Jie Bai 1 1 5 0 0.068
506 Jie Su 1 1 5 0 0.068
507 Jing Cui 1 1 5 0 0.068
508 Joseph Cole 1 1 5 0 0.068
509 Jun Fu 1 1 5 0 0.068
510 Jun Ren 1 1 5 0 0.068
511 Jun Wen 1 1 5 0 0.068
512 Li Li 1 1 5 0 0.068
513 Li Qiao 1 1 5 0 0.068
514 Lisa Pennington 1 1 5 0 0.068
515 Liwia Piejko 1 1 5 0 0.068
516 Min Tian 1 1 5 0 0.068
517 Min Xiong 1 1 5 0 0.068
518 Ming Qiu 1 1 5 0 0.068
519 Na Han 1 1 5 0 0.068
520 Nicole Thomas 1 1 5 0 0.068
521 Ping Meng 1 1 5 0 0.068
522 Ping Tao 1 1 5 0 0.068
523 Rachel Ford 1 1 5 0 0.068
524 Ryan Adams 1 1 5 0 0.068
525 Stella Fusani 1 1 5 0 0.068
526 Tao Xiong 1 1 5 0 0.068
527 Tao Yin 1 1 5 0 0.068
528 Xiuying Shao 1 1 5 0 0.068
529 Yan Huang 1 1 5 0 0.068
530 Yang Lei 1 1 5 0 0.068
531 Yong Wang 1 1 5 0 0.068
532 Jennifer Alexander 1 0 8 1 0.065
533 Lauren Steele 1 0 8 1 0.065
534 Lori Young 1 0 8 1 0.065
535 Sean Riggs 1 0 8 1 0.065
536 Abigail Hawkins 1 1 4 0 0.063
537 Amanda Coleman 1 1 4 0 0.063
538 Andrew Lee 1 1 4 0 0.063
539 Carly Luna 1 1 4 0 0.063
540 Chao Cheng 1 1 4 0 0.063
541 Chao Lu 1 1 4 0 0.063
542 Chao Sun 1 1 4 0 0.063
543 Charles Vazquez 1 1 4 0 0.063
544 Chelsea Harris 1 1 4 0 0.063
545 David Morgan 1 1 4 0 0.063
546 Devin Chan 1 1 4 0 0.063
547 Donna Ryan 1 1 4 0 0.063
548 Fang Luo 1 1 4 0 0.063
549 Fang Zhou 1 1 4 0 0.063
550 Frédéric de la Coulon 1 1 4 0 0.063
551 Gang Lai 1 1 4 0 0.063
552 Gang Tian 1 1 4 0 0.063
553 Gang Yuan 1 1 4 0 0.063
554 Guiying Fan 1 1 4 0 0.063
555 Guiying Yang 1 1 4 0 0.063
556 Jenna York 1 1 4 0 0.063
557 Jennifer Harris 1 1 4 0 0.063
558 Jie Zhong 1 1 4 0 0.063
559 Jing Xiang 1 1 4 0 0.063
560 Juan Chen 1 1 4 0 0.063
561 Juan Han 1 1 4 0 0.063
562 Juan Wang 1 1 4 0 0.063
563 Jörg Niemeier 1 1 4 0 0.063
564 Kimberly Moore 1 1 4 0 0.063
565 Kinship Harmony 1 1 4 0 0.063
566 Lei Peng 1 1 4 0 0.063
567 Lei Tang 1 1 4 0 0.063
568 Lei Xue 1 1 4 0 0.063
569 Li Bai 1 1 4 0 0.063
570 Li Deng 1 1 4 0 0.063
571 Lia Grant 1 1 4 0 0.063
572 Mary King 1 1 4 0 0.063
573 Mary Parsons 1 1 4 0 0.063
574 Maurice Harrison 1 1 4 0 0.063
575 Michael Gregory 1 1 4 0 0.063
576 Michael Jefferson 1 1 4 0 0.063
577 Michelle Weiss 1 1 4 0 0.063
578 Min Xia 1 1 4 0 0.063
579 Ming Liu 1 1 4 0 0.063
580 Mitchell Bryan 1 1 4 0 0.063
581 Paul-Louis Benard 1 1 4 0 0.063
582 Ping Chang 1 1 4 0 0.063
583 Ping Feng 1 1 4 0 0.063
584 Ping Wu 1 1 4 0 0.063
585 Ping Xu 1 1 4 0 0.063
586 Ricardo Ward 1 1 4 0 0.063
587 Scott Burke 1 1 4 0 0.063
588 Tao Du 1 1 4 0 0.063
589 Tao Zou 1 1 4 0 0.063
590 The Nulls 1 1 4 0 0.063
591 Valérie Mathieu 1 1 4 0 0.063
592 Victoria Vazquez 1 1 4 0 0.063
593 Xia Ye 1 1 4 0 0.063
594 Xia Zhao 1 1 4 0 0.063
595 Xiulan Hu 1 1 4 0 0.063
596 Xiulan Lu 1 1 4 0 0.063
597 Yan Yan 1 1 4 0 0.063
598 Yang Mo 1 1 4 0 0.063
599 Yang Shao 1 1 4 0 0.063
600 Yong Wu 1 1 4 0 0.063
601 Zachary Cole 1 1 4 0 0.063
602 Éric Lamy 1 1 4 0 0.063
603 Amber Ramsey 1 1 3 0 0.057
604 Amedeo Mimun 1 1 3 0 0.057
605 Angela Perez 1 1 3 0 0.057
606 Astrid Nørgaard 1 1 3 0 0.057
607 Chad Marks 1 1 3 0 0.057
608 Christine Brown 1 1 3 0 0.057
609 Christine Jones 1 1 3 0 0.057
610 Christopher Reilly 1 1 3 0 0.057
611 Daniel Oneill 1 1 3 0 0.057
612 Deborah Norton 1 1 3 0 0.057
613 Edward Little 1 1 3 0 0.057
614 Fang Xiang 1 1 3 0 0.057
615 Gang Shen 1 1 3 0 0.057
616 Ida Vigliotti 1 1 3 0 0.057
617 Jacob Arnold 1 1 3 0 0.057
618 Jeffrey Watson 1 1 3 0 0.057
619 Jie Cheng 1 1 3 0 0.057
620 Jimmy Arnold 1 1 3 0 0.057
621 Jing Xiao 1 1 3 0 0.057
622 Juan Cheng 1 1 3 0 0.057
623 Juan Deng 1 1 3 0 0.057
624 Juan Kang 1 1 3 0 0.057
625 Jun Hao 1 1 3 0 0.057
626 Jun Wang 1 1 3 0 0.057
627 Jun Ye 1 1 3 0 0.057
628 Jun Zou 1 1 3 0 0.057
629 Latasha Watts 1 1 3 0 0.057
630 Lei Su 1 1 3 0 0.057
631 Lei Zheng 1 1 3 0 0.057
632 Li Zhou 1 1 3 0 0.057
633 Margherita Giammusso 1 1 3 0 0.057
634 Monica Murphy 1 1 3 0 0.057
635 Pina Garrone 1 1 3 0 0.057
636 Ping Bai 1 1 3 0 0.057
637 Ping Song 1 1 3 0 0.057
638 Qiang Han 1 1 3 0 0.057
639 Qiang Mo 1 1 3 0 0.057
640 Susan Randall 1 1 3 0 0.057
641 Tao Shen 1 1 3 0 0.057
642 Xiulan Lai 1 1 3 0 0.057
643 Yan Wang 1 1 3 0 0.057
644 Yan Zhu 1 1 3 0 0.057
645 Yang Qian 1 1 3 0 0.057
646 Yong Feng 1 1 3 0 0.057
647 Andrew Carrillo 1 1 2 0 0.052
648 Chao Mo 1 1 2 0 0.052
649 Douglas Roberts 1 1 2 0 0.052
650 Fiona Mercer 1 1 2 0 0.052
651 Gang Lin 1 1 2 0 0.052
652 Gang Pan 1 1 2 0 0.052
653 Isaiah Morales 1 1 2 0 0.052
654 Jason Vaughn 1 1 2 0 0.052
655 Jill Smith 1 1 2 0 0.052
656 John Maldonado 1 1 2 0 0.052
657 Juan Gong 1 1 2 0 0.052
658 Jun Yu 1 1 2 0 0.052
659 Katelyn Johnson 1 1 2 0 0.052
660 Kyle Lawrence 1 1 2 0 0.052
661 Li Wen 1 1 2 0 0.052
662 Li Xiong 1 1 2 0 0.052
663 Liam O'Sullivan 1 1 2 0 0.052
664 Mary Morales 1 1 2 0 0.052
665 Michael Snyder 1 1 2 0 0.052
666 Michaela Brooks 1 1 2 0 0.052
667 Michelangelo Berlusconi 1 1 2 0 0.052
668 Ming Zhou 1 1 2 0 0.052
669 Miss Erin Dean 1 1 2 0 0.052
670 Na Lai 1 1 2 0 0.052
671 Nancy Arnold 1 1 2 0 0.052
672 Raymond Martin 1 1 2 0 0.052
673 Richard Mckenzie 1 1 2 0 0.052
674 Stephanie Benjamin 1 1 2 0 0.052
675 Stephen Meyer 1 1 2 0 0.052
676 Steven Williams 1 1 2 0 0.052
677 Summer Bender 1 1 2 0 0.052
678 Tao Kong 1 1 2 0 0.052
679 Wei Zou 1 1 2 0 0.052
680 Xia Yan 1 1 2 0 0.052
681 Xiulan Liang 1 1 2 0 0.052
682 Xiulan Zhang 1 1 2 0 0.052
683 Xiuying Xie 1 1 2 0 0.052
684 Yan Tao 1 1 2 0 0.052
685 Yang Zhong 1 1 2 0 0.052
686 Yong Ding 1 1 2 0 0.052
687 Yong Tan 1 1 2 0 0.052
688 Bronze Tiger 1 0 5 1 0.049
689 Dustin Maxwell 1 1 1 0 0.047
690 Erin Martinez 1 1 1 0 0.047
691 Ishaan Bal 1 1 1 0 0.047
692 Isla Quinn 1 1 1 0 0.047
693 Jessica Carney 1 1 1 0 0.047
694 Jun Bai 1 1 1 0 0.047
695 Lei Kong 1 1 1 0 0.047
696 Michael Smith 1 1 1 0 0.047
697 Ping Zhao 1 1 1 0 0.047
698 Riya Dhingra 1 1 1 0 0.047
699 Shlok Cherian 1 1 1 0 0.047
700 Tanya Garrett 1 1 1 0 0.047
701 Tara Deshmukh 1 1 1 0 0.047
702 William Knapp 1 1 1 0 0.047
703 Xiulan Hao 1 1 1 0 0.047
704 Xiuying Liang 1 1 1 0 0.047
705 Yang Gong 1 1 1 0 0.047
706 Jing Feng 1 1 0 0 0.042
707 Guiying Gong 1 0 6 0 0.032
708 Jing Jin 1 0 6 0 0.032
709 Ming Deng 1 0 6 0 0.032
710 Ming Jia 1 0 6 0 0.032
711 Tao Xue 1 0 6 0 0.032
712 Whispered Resonance 1 0 6 0 0.032
713 Xiuying Fang 1 0 6 0 0.032
714 Gang Xiong 1 0 3 0 0.016
715 Li Huang 1 0 3 0 0.016
716 Qiang Luo 1 0 3 0 0.016
717 Yong Ye 1 0 3 0 0.016
718 Jonathan Morris 1 0 2 0 0.011
719 Ming Xiao 1 0 2 0 0.011
720 Xiuying Wei 1 0 2 0 0.011

4.3.2 Visualizing the career of Sailor Shift

Show Code
# Data Preparation

chosen_creator_1 = "Sailor Shift"

# Step 1: Get the node of the chosen creator
chosen_node_1 <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_1) %>%
  pull(creator_from) %>%
  unique()

# Step 2: Get the songs that the top creator produced
creator_songs_1 <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_1) %>%
  pull(song_to)

# Step 3: Get the songs they have influenced / artists they collaborate with
creators_songs_collaborate_influence_1 <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_1,
         infuence_music_collaborate != chosen_node_1) %>%
  pull(infuence_music_collaborate)

# Step 4: Get the songs they have influenced
creators_songs_influence_1 <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_1,
         infuence_music_collaborate != chosen_node_1,
         `Edge Colour` == "Influenced By") %>%
  pull(infuence_music_collaborate)

# Step 5: Get the influenced creators of the influenced songs
creators_songs_influence_creators_1 <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_1,
         influence_creator != chosen_node_1,
         !is.na(influence_creator),
         infuence_music_collaborate %in% creators_songs_influence_1) %>%
  pull(influence_creator)

all_nodes <- unique(c(chosen_node_1, 
                      creator_songs_1, 
                      creators_songs_collaborate_influence_1,
                      creators_songs_influence_creators_1))

# Create subgraph
sub_graph <- graph %>%
  filter(name %in% all_nodes)



# Visualisation

g <- sub_graph %>%
  ggraph(layout = "fr") + 
  geom_edge_fan(
    aes(
      edge_colour = `Edge Colour`,
      start_cap = circle(1, 'mm'),
      end_cap = circle(1, 'mm')
      ),
    arrow = arrow(length = unit(1, 'mm')),
    alpha = 0.3
  ) +
  geom_point_interactive(
    aes(
      x = x,
      y = y,
      data_id = name,
      colour = `Node Colour`,
      shape = `Node Type`,
      size = ifelse(node_name == chosen_creator_1, 3, 1),
      tooltip = case_when(
        `Node Type` == "Album" ~ sprintf(
          "%s<br/>%s<br/>Notable: %s<br/>(%s)", node_name, genre, notable, release_date
        ),
        `Node Type` == "Song" ~ sprintf(
          "%s<br/>%s<br/>Notable: %s<br/>(%s)<br/>Single: %s", node_name, genre, notable, release_date, single
        ),
        TRUE ~ sprintf("%s", node_name)
      )
    ),
    show.legend = c(size = FALSE)
  )+ 
  geom_node_text(
    aes(
      label = ifelse(node_name == chosen_creator_1, chosen_creator_1, NA)
    ),
    fontface = "bold",
    size = 2.5,
    colour = 'red',
    show.legend = FALSE
  ) +
  scale_shape_manual(
    name = "Node Type",
    values = c(
      "Album" = 16,
      "MusicalGroup" = 15,
      "Person" = 17,
      "Song" = 10
    )
  ) +
  scale_edge_colour_manual(
    name = "Edge Colour",
    values = c(
      `Creator Of` = "#47D45A",
      `Influenced By` = "#FF5757",
      `Member Of` = "#CF57FF"
    )
  ) +
  scale_colour_manual(
    name = "Node Colour",
    values = c(
      "Musician" = "grey50",
      "Oceanus Folk" = "#0027EA",
      "Other Genre" = "#A45200"
    )
  ) +
  theme_graph() +
  theme(legend.text = element_text(size = 6),
        legend.title = element_text(size = 9)) +
  scale_size_identity()

girafe(ggobj = g, width_svg = 7, height_svg = 6)
Show Code
# Data Preparation

# Step 1: Count number of music by release date
music_by_date_1 <- mc1_nodes_clean %>%
  filter(name %in% unique(creator_songs_1)) %>%
  count(release_date, name = "music_count") %>%
  arrange(release_date) %>%  # Ensure dates are in chronological order
  mutate(cumulative_count = cumsum(music_count))



# Visualisation

plot_ly(
  data = music_by_date_1,
  x = ~release_date,
  y = ~music_count,
  type = "bar",
  name = "Number of Music Releases",
  marker = list(color = "#2E3192"),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Released Date: ", release_date,
    "<br>Count: ", music_count
    )
  ) %>%
  add_trace(
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = "Cumulative Count",
    line = list(color = "black", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Released Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Yearly Music (Song/Album) Releases",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )
Show Code
# Data Preparation

# Step 1: Count number of notable music by release date
notable_music_by_date_1 <- mc1_nodes_clean %>%
  filter(name %in% unique(creator_songs_1), notable == TRUE) %>%
  count(release_date, name = "music_count") %>%
  arrange(release_date) %>%  # Ensure dates are in chronological order
  mutate(cumulative_count = cumsum(music_count))



# Visualisation

plot_ly(
  data = notable_music_by_date_1,
  x = ~release_date,
  y = ~music_count,
  type = "bar",
  name = "Number of Notable Music Releases",
  marker = list(color = "#2E3192"),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Released Date: ", release_date,
    "<br>Count: ", music_count
    )
  ) %>%
  add_trace(
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = "Cumulative Count",
    line = list(color = "black", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Released Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Yearly Notable Music (Song/Album) Releases",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )
Show Code
# Data Preparation

# Step 1: Count number of influenced artists by release date
influence_artists_by_date_1 <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_from %in% unique(chosen_node_1),
         influence_creator != unique(chosen_node_1)) %>%
  # Get unique artist-date pairs first
  distinct(influence_creator, influence_release_date) %>%
  # Find first influence date for each artist
  group_by(influence_creator) %>%
  summarize(
    first_influence_date = if(n() > 0) min(influence_release_date) else NA_real_,
    .groups = "drop"
  ) %>%
  # Count new artists by first influence date
  count(first_influence_date, name = "music_count") %>%
  arrange(first_influence_date) %>%
  rename(creator_release_date = first_influence_date) %>%
  # Calculate cumulative unique artists
  mutate(cumulative_count = cumsum(music_count))



# Visualisation

plot_ly(
  data = influence_artists_by_date_1,
  x = ~creator_release_date,
  y = ~music_count,
  type = "bar",
  name = "Number of Influenced Artists",
  marker = list(color = "#2E3192"),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Influence Date: ", creator_release_date,
    "<br>Count: ", music_count
    )
  ) %>%
  add_trace(
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = "Cumulative Count",
    line = list(color = "black", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Influence Date: ", creator_release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Annual Count of New Artist Influences & Collaborations",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )
Show Code
# Data Preparation

# Step 1: Count number of influenced music by release date
influence_song_by_date_1 <- mc1_nodes_clean %>%
  filter(name %in% unique(creators_songs_influence_1)) %>%
  count(release_date, name = "music_count") %>%
  arrange(release_date) %>%  # Ensure dates are in chronological order
  mutate(cumulative_count = cumsum(music_count))



# Visualisation

plot_ly(
  data = influence_song_by_date_1,
  x = ~release_date,
  y = ~music_count,
  type = "bar",
  name = "Number of Influenced Music",
  marker = list(color = "#2E3192"),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Influence Date: ", release_date,
    "<br>Count: ", music_count
    )
  ) %>%
  add_trace(
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = "Cumulative Count",
    line = list(color = "black", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Influence Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Yearly Music (Song/Album) Influence",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )

4.3.3 Visualizing the career of Chao Wu

Show Code
# Data Preparation

chosen_creator_2 = "Chao Wu"

# Step 1: Get the node of the chosen creator
chosen_node_2 <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_2) %>%
  pull(creator_from) %>%
  unique()

# Step 2: Get the songs that the top creator produced
creator_songs_2 <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_2) %>%
  pull(song_to)

# Step 3: Get the songs they have influenced / artists they collaborate with
creators_songs_collaborate_influence_2 <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_2,
         infuence_music_collaborate != chosen_node_2) %>%
  pull(infuence_music_collaborate)

# Step 4: Get the songs they have influenced
creators_songs_influence_2 <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_2,
         infuence_music_collaborate != chosen_node_2,
         `Edge Colour` == "Influenced By") %>%
  pull(infuence_music_collaborate)

# Step 5: Get the influenced creators of the influenced songs
creators_songs_influence_creators_2 <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_2,
         influence_creator != chosen_node_2,
         !is.na(influence_creator),
         infuence_music_collaborate %in% creators_songs_influence_2) %>%
  pull(influence_creator)

all_nodes <- unique(c(chosen_node_2, 
                      creator_songs_2, 
                      creators_songs_collaborate_influence_2,
                      creators_songs_influence_creators_2))

# Create subgraph
sub_graph <- graph %>%
  filter(name %in% all_nodes)



# Visualisation

g <- sub_graph %>%
  ggraph(layout = "fr") + 
  geom_edge_fan(
    aes(
      edge_colour = `Edge Colour`,
      start_cap = circle(1, 'mm'),
      end_cap = circle(1, 'mm')
      ),
    arrow = arrow(length = unit(1, 'mm')),
    alpha = 0.3
  ) +
  geom_point_interactive(
    aes(
      x = x,
      y = y,
      data_id = name,
      colour = `Node Colour`,
      shape = `Node Type`,
      size = ifelse(node_name == chosen_creator_2, 3, 1),
      tooltip = case_when(
        `Node Type` == "Album" ~ sprintf(
          "%s<br/>%s<br/>Notable: %s<br/>(%s)", node_name, genre, notable, release_date
        ),
        `Node Type` == "Song" ~ sprintf(
          "%s<br/>%s<br/>Notable: %s<br/>(%s)<br/>Single: %s", node_name, genre, notable, release_date, single
        ),
        TRUE ~ sprintf("%s", node_name)
      )
    ),
    show.legend = c(size = FALSE)
  )+ 
  geom_node_text(
    aes(
      label = ifelse(node_name == chosen_creator_2, chosen_creator_2, NA)
    ),
    fontface = "bold",
    size = 2.5,
    colour = 'red',
    show.legend = FALSE
  ) +
  scale_shape_manual(
    name = "Node Type",
    values = c(
      "Album" = 16,
      "MusicalGroup" = 15,
      "Person" = 17,
      "Song" = 10
    )
  ) +
  scale_edge_colour_manual(
    name = "Edge Colour",
    values = c(
      `Creator Of` = "#47D45A",
      `Influenced By` = "#FF5757",
      `Member Of` = "#CF57FF"
    )
  ) +
  scale_colour_manual(
    name = "Node Colour",
    values = c(
      "Musician" = "grey50",
      "Oceanus Folk" = "#0027EA",
      "Other Genre" = "#A45200"
    )
  ) +
  theme_graph() +
  theme(legend.text = element_text(size = 6),
        legend.title = element_text(size = 9)) +
  scale_size_identity()

girafe(ggobj = g, width_svg = 7, height_svg = 6)
Show Code
# Data Preparation

# Step 1: Count number of music by release date
music_by_date_2 <- mc1_nodes_clean %>%
  filter(name %in% unique(creator_songs_2)) %>%
  count(release_date, name = "music_count") %>%
  arrange(release_date) %>%  # Ensure dates are in chronological order
  mutate(cumulative_count = cumsum(music_count))



# Visualisation

plot_ly(
  data = music_by_date_2,
  x = ~release_date,
  y = ~music_count,
  type = "bar",
  name = "Number of Music Releases",
  marker = list(color = "#2E3192"),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Released Date: ", release_date,
    "<br>Count: ", music_count
    )
  ) %>%
  add_trace(
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = "Cumulative Count",
    line = list(color = "black", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Released Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Yearly Music (Song/Album) Releases",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )
Show Code
# Data Preparation

# Step 1: Count number of notable music by release date
notable_music_by_date_2 <- mc1_nodes_clean %>%
  filter(name %in% unique(creator_songs_2), notable == TRUE) %>%
  count(release_date, name = "music_count") %>%
  arrange(release_date) %>%  # Ensure dates are in chronological order
  mutate(cumulative_count = cumsum(music_count))



# Visualisation

plot_ly(
  data = notable_music_by_date_2,
  x = ~release_date,
  y = ~music_count,
  type = "bar",
  name = "Number of Notable Music Releases",
  marker = list(color = "#2E3192"),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Released Date: ", release_date,
    "<br>Count: ", music_count
    )
  ) %>%
  add_trace(
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = "Cumulative Count",
    line = list(color = "black", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Released Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Yearly Notable Music (Song/Album) Releases",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )
Show Code
# Data Preparation

# Step 1: Count number of influenced artists by release date
influence_artists_by_date_2 <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_from %in% unique(chosen_node_2),
         influence_creator != unique(chosen_node_2)) %>%
  # Get unique artist-date pairs first
  distinct(influence_creator, influence_release_date) %>%
  # Find first influence date for each artist
  group_by(influence_creator) %>%
  summarize(
    first_influence_date = if(n() > 0) min(influence_release_date) else NA_real_,
    .groups = "drop"
  ) %>%
  # Count new artists by first influence date
  count(first_influence_date, name = "music_count") %>%
  arrange(first_influence_date) %>%
  rename(creator_release_date = first_influence_date) %>%
  # Calculate cumulative unique artists
  mutate(cumulative_count = cumsum(music_count))



# Visualisation

plot_ly(
  data = influence_artists_by_date_2,
  x = ~creator_release_date,
  y = ~music_count,
  type = "bar",
  name = "Number of Influenced Artists",
  marker = list(color = "#2E3192"),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Influence Date: ", creator_release_date,
    "<br>Count: ", music_count
    )
  ) %>%
  add_trace(
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = "Cumulative Count",
    line = list(color = "black", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Influence Date: ", creator_release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Annual Count of New Artist Influences & Collaborations",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )
Show Code
# Data Preparation

# Step 1: Count number of influenced music by release date
influence_song_by_date_2 <- mc1_nodes_clean %>%
  filter(name %in% unique(creators_songs_influence_2)) %>%
  count(release_date, name = "music_count") %>%
  arrange(release_date) %>%  # Ensure dates are in chronological order
  mutate(cumulative_count = cumsum(music_count))



# Visualisation

plot_ly(
  data = influence_song_by_date_2,
  x = ~release_date,
  y = ~music_count,
  type = "bar",
  name = "Number of Influenced Music",
  marker = list(color = "#2E3192"),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Influence Date: ", release_date,
    "<br>Count: ", music_count
    )
  ) %>%
  add_trace(
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = "Cumulative Count",
    line = list(color = "black", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Influence Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Yearly Music (Song/Album) Influence",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )

4.3.4 Visualizing the career of Xia Jia

Show Code
# Data Preparation

chosen_creator_3 = "Xia Jia"

# Step 1: Get the node of the chosen creator
chosen_node_3 <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_3) %>%
  pull(creator_from) %>%
  unique()

# Step 2: Get the songs that the top creator produced
creator_songs_3 <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_3) %>%
  pull(song_to)

# Step 3: Get the songs they have influenced / artists they collaborate with
creators_songs_collaborate_influence_3 <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_3,
         infuence_music_collaborate != chosen_node_3) %>%
  pull(infuence_music_collaborate)

# Step 4: Get the songs they have influenced
creators_songs_influence_3 <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_3,
         infuence_music_collaborate != chosen_node_3,
         `Edge Colour` == "Influenced By") %>%
  pull(infuence_music_collaborate)

# Step 5: Get the influenced creators of the influenced songs
creators_songs_influence_creators_3 <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_3,
         influence_creator != chosen_node_3,
         !is.na(influence_creator),
         infuence_music_collaborate %in% creators_songs_influence_3) %>%
  pull(influence_creator)

all_nodes <- unique(c(chosen_node_3, 
                      creator_songs_3, 
                      creators_songs_collaborate_influence_3,
                      creators_songs_influence_creators_3))

# Create subgraph
sub_graph <- graph %>%
  filter(name %in% all_nodes)



# Visualisation

g <- sub_graph %>%
  ggraph(layout = "fr") + 
  geom_edge_fan(
    aes(
      edge_colour = `Edge Colour`,
      start_cap = circle(1, 'mm'),
      end_cap = circle(1, 'mm')
      ),
    arrow = arrow(length = unit(1, 'mm')),
    alpha = 0.3
  ) +
  geom_point_interactive(
    aes(
      x = x,
      y = y,
      data_id = name,
      colour = `Node Colour`,
      shape = `Node Type`,
      size = ifelse(node_name == chosen_creator_3, 3, 1),
      tooltip = case_when(
        `Node Type` == "Album" ~ sprintf(
          "%s<br/>%s<br/>Notable: %s<br/>(%s)", node_name, genre, notable, release_date
        ),
        `Node Type` == "Song" ~ sprintf(
          "%s<br/>%s<br/>Notable: %s<br/>(%s)<br/>Single: %s", node_name, genre, notable, release_date, single
        ),
        TRUE ~ sprintf("%s", node_name)
      )
    ),
    show.legend = c(size = FALSE)
  )+ 
  geom_node_text(
    aes(
      label = ifelse(node_name == chosen_creator_3, chosen_creator_3, NA)
    ),
    fontface = "bold",
    size = 2.5,
    colour = 'red',
    show.legend = FALSE
  ) +
  scale_shape_manual(
    name = "Node Type",
    values = c(
      "Album" = 16,
      "MusicalGroup" = 15,
      "Person" = 17,
      "Song" = 10
    )
  ) +
  scale_edge_colour_manual(
    name = "Edge Colour",
    values = c(
      `Creator Of` = "#47D45A",
      `Influenced By` = "#FF5757",
      `Member Of` = "#CF57FF"
    )
  ) +
  scale_colour_manual(
    name = "Node Colour",
    values = c(
      "Musician" = "grey50",
      "Oceanus Folk" = "#0027EA",
      "Other Genre" = "#A45200"
    )
  ) +
  theme_graph() +
  theme(legend.text = element_text(size = 6),
        legend.title = element_text(size = 9)) +
  scale_size_identity()

girafe(ggobj = g, width_svg = 7, height_svg = 6)
Show Code
# Data Preparation

# Step 1: Count number of music by release date
music_by_date_3 <- mc1_nodes_clean %>%
  filter(name %in% unique(creator_songs_3)) %>%
  count(release_date, name = "music_count") %>%
  arrange(release_date) %>%  # Ensure dates are in chronological order
  mutate(cumulative_count = cumsum(music_count))



# Visualisation

plot_ly(
  data = music_by_date_3,
  x = ~release_date,
  y = ~music_count,
  type = "bar",
  name = "Number of Music Releases",
  marker = list(color = "#2E3192"),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Released Date: ", release_date,
    "<br>Count: ", music_count
    )
  ) %>%
  add_trace(
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = "Cumulative Count",
    line = list(color = "black", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Released Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Yearly Music (Song/Album) Releases",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )
Show Code
# Data Preparation

# Step 1: Count number of notable music by release date
notable_music_by_date_3 <- mc1_nodes_clean %>%
  filter(name %in% unique(creator_songs_3), notable == TRUE) %>%
  count(release_date, name = "music_count") %>%
  arrange(release_date) %>%  # Ensure dates are in chronological order
  mutate(cumulative_count = cumsum(music_count))



# Visualisation

plot_ly(
  data = notable_music_by_date_3,
  x = ~release_date,
  y = ~music_count,
  type = "bar",
  name = "Number of Notable Music Releases",
  marker = list(color = "#2E3192"),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Released Date: ", release_date,
    "<br>Count: ", music_count
    )
  ) %>%
  add_trace(
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = "Cumulative Count",
    line = list(color = "black", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Released Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Yearly Notable Music (Song/Album) Releases",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )
Show Code
# Data Preparation

# Step 1: Count number of influenced artists by release date
influence_artists_by_date_3 <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_from %in% unique(chosen_node_3),
         influence_creator != unique(chosen_node_3)) %>%
  # Get unique artist-date pairs first
  distinct(influence_creator, influence_release_date) %>%
  # Find first influence date for each artist
  group_by(influence_creator) %>%
  summarize(
    first_influence_date = if(n() > 0) min(influence_release_date) else NA_real_,
    .groups = "drop"
  ) %>%
  # Count new artists by first influence date
  count(first_influence_date, name = "music_count") %>%
  arrange(first_influence_date) %>%
  rename(creator_release_date = first_influence_date) %>%
  # Calculate cumulative unique artists
  mutate(cumulative_count = cumsum(music_count))



# Visualisation

plot_ly(
  data = influence_artists_by_date_3,
  x = ~creator_release_date,
  y = ~music_count,
  type = "bar",
  name = "Number of Influenced Artists",
  marker = list(color = "#2E3192"),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Influence Date: ", creator_release_date,
    "<br>Count: ", music_count
    )
  ) %>%
  add_trace(
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = "Cumulative Count",
    line = list(color = "black", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Influence Date: ", creator_release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Annual Count of New Artist Influences & Collaborations",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )
Show Code
# Data Preparation

# Step 1: Count number of influenced music by release date
influence_song_by_date_3 <- mc1_nodes_clean %>%
  filter(name %in% unique(creators_songs_influence_3)) %>%
  count(release_date, name = "music_count") %>%
  arrange(release_date) %>%  # Ensure dates are in chronological order
  mutate(cumulative_count = cumsum(music_count))



# Visualisation

plot_ly(
  data = influence_song_by_date_3,
  x = ~release_date,
  y = ~music_count,
  type = "bar",
  name = "Number of Influenced Music",
  marker = list(color = "#2E3192"),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Influence Date: ", release_date,
    "<br>Count: ", music_count
    )
  ) %>%
  add_trace(
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = "Cumulative Count",
    line = list(color = "black", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Influence Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Yearly Music (Song/Album) Influence",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )

4.3.5 Visualizing the career of Donna Caldwell

Show Code
# Data Preparation

chosen_creator_4 = "Donna Caldwell"

# Step 1: Get the node of the chosen creator
chosen_node_4 <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_4) %>%
  pull(creator_from) %>%
  unique()

# Step 2: Get the songs that the top creator produced
creator_songs_4 <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_4) %>%
  pull(song_to)

# Step 3: Get the songs they have influenced / artists they collaborate with
creators_songs_collaborate_influence_4 <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_4,
         infuence_music_collaborate != chosen_node_4) %>%
  pull(infuence_music_collaborate)

# Step 4: Get the songs they have influenced
creators_songs_influence_4 <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_4,
         infuence_music_collaborate != chosen_node_4,
         `Edge Colour` == "Influenced By") %>%
  pull(infuence_music_collaborate)

# Step 5: Get the influenced creators of the influenced songs
creators_songs_influence_creators_4 <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_4,
         influence_creator != chosen_node_4,
         !is.na(influence_creator),
         infuence_music_collaborate %in% creators_songs_influence_4) %>%
  pull(influence_creator)

all_nodes <- unique(c(chosen_node_4, 
                      creator_songs_4, 
                      creators_songs_collaborate_influence_4,
                      creators_songs_influence_creators_4))

# Create subgraph
sub_graph <- graph %>%
  filter(name %in% all_nodes)



# Visualisation

g <- sub_graph %>%
  ggraph(layout = "fr") + 
  geom_edge_fan(
    aes(
      edge_colour = `Edge Colour`,
      start_cap = circle(1, 'mm'),
      end_cap = circle(1, 'mm')
      ),
    arrow = arrow(length = unit(1, 'mm')),
    alpha = 0.3
  ) +
  geom_point_interactive(
    aes(
      x = x,
      y = y,
      data_id = name,
      colour = `Node Colour`,
      shape = `Node Type`,
      size = ifelse(node_name == chosen_creator_4, 3, 1),
      tooltip = case_when(
        `Node Type` == "Album" ~ sprintf(
          "%s<br/>%s<br/>Notable: %s<br/>(%s)", node_name, genre, notable, release_date
        ),
        `Node Type` == "Song" ~ sprintf(
          "%s<br/>%s<br/>Notable: %s<br/>(%s)<br/>Single: %s", node_name, genre, notable, release_date, single
        ),
        TRUE ~ sprintf("%s", node_name)
      )
    ),
    show.legend = c(size = FALSE)
  )+ 
  geom_node_text(
    aes(
      label = ifelse(node_name == chosen_creator_4, chosen_creator_4, NA)
    ),
    fontface = "bold",
    size = 2.5,
    colour = 'red',
    show.legend = FALSE
  ) +
  scale_shape_manual(
    name = "Node Type",
    values = c(
      "Album" = 16,
      "MusicalGroup" = 15,
      "Person" = 17,
      "Song" = 10
    )
  ) +
  scale_edge_colour_manual(
    name = "Edge Colour",
    values = c(
      `Creator Of` = "#47D45A",
      `Influenced By` = "#FF5757",
      `Member Of` = "#CF57FF"
    )
  ) +
  scale_colour_manual(
    name = "Node Colour",
    values = c(
      "Musician" = "grey50",
      "Oceanus Folk" = "#0027EA",
      "Other Genre" = "#A45200"
    )
  ) +
  theme_graph() +
  theme(legend.text = element_text(size = 6),
        legend.title = element_text(size = 9)) +
  scale_size_identity()

girafe(ggobj = g, width_svg = 7, height_svg = 6)
Show Code
# Data Preparation

# Step 1: Count number of music by release date
music_by_date_4 <- mc1_nodes_clean %>%
  filter(name %in% unique(creator_songs_4)) %>%
  count(release_date, name = "music_count") %>%
  arrange(release_date) %>%  # Ensure dates are in chronological order
  mutate(cumulative_count = cumsum(music_count))



# Visualisation

plot_ly(
  data = music_by_date_4,
  x = ~release_date,
  y = ~music_count,
  type = "bar",
  name = "Number of Music Releases",
  marker = list(color = "#2E3192"),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Released Date: ", release_date,
    "<br>Count: ", music_count
    )
  ) %>%
  add_trace(
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = "Cumulative Count",
    line = list(color = "black", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Released Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Yearly Music (Song/Album) Releases",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )
Show Code
# Data Preparation

# Step 1: Count number of notable music by release date
notable_music_by_date_4 <- mc1_nodes_clean %>%
  filter(name %in% unique(creator_songs_4), notable == TRUE) %>%
  count(release_date, name = "music_count") %>%
  arrange(release_date) %>%  # Ensure dates are in chronological order
  mutate(cumulative_count = cumsum(music_count))



# Visualisation

plot_ly(
  data = notable_music_by_date_4,
  x = ~release_date,
  y = ~music_count,
  type = "bar",
  name = "Number of Notable Music Releases",
  marker = list(color = "#2E3192"),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Released Date: ", release_date,
    "<br>Count: ", music_count
    )
  ) %>%
  add_trace(
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = "Cumulative Count",
    line = list(color = "black", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Released Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Yearly Notable Music (Song/Album) Releases",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )
Show Code
# Data Preparation

# Step 1: Count number of influenced artists by release date
influence_artists_by_date_4 <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_from %in% unique(chosen_node_4),
         influence_creator != unique(chosen_node_4)) %>%
  # Get unique artist-date pairs first
  distinct(influence_creator, influence_release_date) %>%
  # Find first influence date for each artist
  group_by(influence_creator) %>%
  summarize(
    first_influence_date = if(n() > 0) min(influence_release_date) else NA_real_,
    .groups = "drop"
  ) %>%
  # Count new artists by first influence date
  count(first_influence_date, name = "music_count") %>%
  arrange(first_influence_date) %>%
  rename(creator_release_date = first_influence_date) %>%
  # Calculate cumulative unique artists
  mutate(cumulative_count = cumsum(music_count))



# Visualisation

plot_ly(
  data = influence_artists_by_date_4,
  x = ~creator_release_date,
  y = ~music_count,
  type = "bar",
  name = "Number of Influenced Artists",
  marker = list(color = "#2E3192"),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Influence Date: ", creator_release_date,
    "<br>Count: ", music_count
    )
  ) %>%
  add_trace(
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = "Cumulative Count",
    line = list(color = "black", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Influence Date: ", creator_release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Annual Count of New Artist Influences & Collaborations",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )
Show Code
# Data Preparation

# Step 1: Count number of influenced music by release date
influence_song_by_date_4 <- mc1_nodes_clean %>%
  filter(name %in% unique(creators_songs_influence_4)) %>%
  count(release_date, name = "music_count") %>%
  arrange(release_date) %>%  # Ensure dates are in chronological order
  mutate(cumulative_count = cumsum(music_count))



# Visualisation

plot_ly(
  data = influence_song_by_date_4,
  x = ~release_date,
  y = ~music_count,
  type = "bar",
  name = "Number of Influenced Music",
  marker = list(color = "#2E3192"),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Influence Date: ", release_date,
    "<br>Count: ", music_count
    )
  ) %>%
  add_trace(
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = "Cumulative Count",
    line = list(color = "black", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Influence Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Yearly Music (Song/Album) Influence",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )

4.3.6 Visualizing the career of Xiulan Ye

Show Code
# Data Preparation

chosen_creator_5 = "Xiulan Ye"

# Step 1: Get the node of the chosen creator
chosen_node_5 <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_5) %>%
  pull(creator_from) %>%
  unique()

# Step 2: Get the songs that the top creator produced
creator_songs_5 <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_5) %>%
  pull(song_to)

# Step 3: Get the songs they have influenced / artists they collaborate with
creators_songs_collaborate_influence_5 <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_5,
         infuence_music_collaborate != chosen_node_5) %>%
  pull(infuence_music_collaborate)

# Step 4: Get the songs they have influenced
creators_songs_influence_5 <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_5,
         infuence_music_collaborate != chosen_node_5,
         `Edge Colour` == "Influenced By") %>%
  pull(infuence_music_collaborate)

# Step 5: Get the influenced creators of the influenced songs
creators_songs_influence_creators_5 <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_5,
         influence_creator != chosen_node_5,
         !is.na(influence_creator),
         infuence_music_collaborate %in% creators_songs_influence_5) %>%
  pull(influence_creator)

all_nodes <- unique(c(chosen_node_5, 
                      creator_songs_5, 
                      creators_songs_collaborate_influence_5,
                      creators_songs_influence_creators_5))

# Create subgraph
sub_graph <- graph %>%
  filter(name %in% all_nodes)



# Visualisation

g <- sub_graph %>%
  ggraph(layout = "fr") + 
  geom_edge_fan(
    aes(
      edge_colour = `Edge Colour`,
      start_cap = circle(1, 'mm'),
      end_cap = circle(1, 'mm')
      ),
    arrow = arrow(length = unit(1, 'mm')),
    alpha = 0.3
  ) +
  geom_point_interactive(
    aes(
      x = x,
      y = y,
      data_id = name,
      colour = `Node Colour`,
      shape = `Node Type`,
      size = ifelse(node_name == chosen_creator_5, 3, 1),
      tooltip = case_when(
        `Node Type` == "Album" ~ sprintf(
          "%s<br/>%s<br/>Notable: %s<br/>(%s)", node_name, genre, notable, release_date
        ),
        `Node Type` == "Song" ~ sprintf(
          "%s<br/>%s<br/>Notable: %s<br/>(%s)<br/>Single: %s", node_name, genre, notable, release_date, single
        ),
        TRUE ~ sprintf("%s", node_name)
      )
    ),
    show.legend = c(size = FALSE)
  )+ 
  geom_node_text(
    aes(
      label = ifelse(node_name == chosen_creator_5, chosen_creator_5, NA)
    ),
    fontface = "bold",
    size = 2.5,
    colour = 'red',
    show.legend = FALSE
  ) +
  scale_shape_manual(
    name = "Node Type",
    values = c(
      "Album" = 16,
      "MusicalGroup" = 15,
      "Person" = 17,
      "Song" = 10
    )
  ) +
  scale_edge_colour_manual(
    name = "Edge Colour",
    values = c(
      `Creator Of` = "#47D45A",
      `Influenced By` = "#FF5757",
      `Member Of` = "#CF57FF"
    )
  ) +
  scale_colour_manual(
    name = "Node Colour",
    values = c(
      "Musician" = "grey50",
      "Oceanus Folk" = "#0027EA",
      "Other Genre" = "#A45200"
    )
  ) +
  theme_graph() +
  theme(legend.text = element_text(size = 6),
        legend.title = element_text(size = 9)) +
  scale_size_identity()

girafe(ggobj = g, width_svg = 7, height_svg = 6)
Show Code
# Data Preparation

# Step 1: Count number of music by release date
music_by_date_5 <- mc1_nodes_clean %>%
  filter(name %in% unique(creator_songs_5)) %>%
  count(release_date, name = "music_count") %>%
  arrange(release_date) %>%  # Ensure dates are in chronological order
  mutate(cumulative_count = cumsum(music_count))



# Visualisation

plot_ly(
  data = music_by_date_5,
  x = ~release_date,
  y = ~music_count,
  type = "bar",
  name = "Number of Music Releases",
  marker = list(color = "#2E3192"),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Released Date: ", release_date,
    "<br>Count: ", music_count
    )
  ) %>%
  add_trace(
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = "Cumulative Count",
    line = list(color = "black", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Released Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Yearly Music (Song/Album) Releases",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )
Show Code
# Data Preparation

# Step 1: Count number of notable music by release date
notable_music_by_date_5 <- mc1_nodes_clean %>%
  filter(name %in% unique(creator_songs_5), notable == TRUE) %>%
  count(release_date, name = "music_count") %>%
  arrange(release_date) %>%  # Ensure dates are in chronological order
  mutate(cumulative_count = cumsum(music_count))



# Visualisation

plot_ly(
  data = notable_music_by_date_5,
  x = ~release_date,
  y = ~music_count,
  type = "bar",
  name = "Number of Notable Music Releases",
  marker = list(color = "#2E3192"),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Released Date: ", release_date,
    "<br>Count: ", music_count
    )
  ) %>%
  add_trace(
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = "Cumulative Count",
    line = list(color = "black", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Released Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Yearly Notable Music (Song/Album) Releases",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )
Show Code
# Data Preparation

# Step 1: Count number of influenced artists by release date
influence_artists_by_date_5 <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_from %in% unique(chosen_node_5),
         influence_creator != unique(chosen_node_5)) %>%
  # Get unique artist-date pairs first
  distinct(influence_creator, influence_release_date) %>%
  # Find first influence date for each artist
  group_by(influence_creator) %>%
  summarize(
    first_influence_date = if(n() > 0) min(influence_release_date) else NA_real_,
    .groups = "drop"
  ) %>%
  # Count new artists by first influence date
  count(first_influence_date, name = "music_count") %>%
  arrange(first_influence_date) %>%
  rename(creator_release_date = first_influence_date) %>%
  # Calculate cumulative unique artists
  mutate(cumulative_count = cumsum(music_count))



# Visualisation

plot_ly(
  data = influence_artists_by_date_5,
  x = ~creator_release_date,
  y = ~music_count,
  type = "bar",
  name = "Number of Influenced Artists",
  marker = list(color = "#2E3192"),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Influence Date: ", creator_release_date,
    "<br>Count: ", music_count
    )
  ) %>%
  add_trace(
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = "Cumulative Count",
    line = list(color = "black", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Influence Date: ", creator_release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Annual Count of New Artist Influences & Collaborations",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )
Show Code
# Data Preparation

# Step 1: Count number of influenced music by release date
influence_song_by_date_5 <- mc1_nodes_clean %>%
  filter(name %in% unique(creators_songs_influence_5)) %>%
  count(release_date, name = "music_count") %>%
  arrange(release_date) %>%  # Ensure dates are in chronological order
  mutate(cumulative_count = cumsum(music_count))



# Visualisation

plot_ly(
  data = influence_song_by_date_5,
  x = ~release_date,
  y = ~music_count,
  type = "bar",
  name = "Number of Influenced Music",
  marker = list(color = "#2E3192"),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Influence Date: ", release_date,
    "<br>Count: ", music_count
    )
  ) %>%
  add_trace(
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = "Cumulative Count",
    line = list(color = "black", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Influence Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Yearly Music (Song/Album) Influence",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )

4.3.7 Compare and contrast the five artists’ rise in popularity and influence

The cumulative graphs of the 5 artists (Sailor Shift, Chao Wu, Xia Jia, Donna Caldwell, and Xiulan Ye) will be overlaid for comparison of their musical careers.

Show Code
# Visualisation

plot_ly(
  data = music_by_date_1,
  x = ~release_date,
  y = ~cumulative_count,
  type = "scatter",
  mode = "lines+markers",
  name = chosen_creator_1,
  line = list(color = "#2E3192", width = 2),
  marker = list(color = "red", size = 6),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Artist: ", chosen_creator_1,
    "<br>Influence Date: ", release_date,
    "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  add_trace(
    data = music_by_date_2,
    x = ~release_date,
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = chosen_creator_2,
    line = list(color = "green", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Artist: ", chosen_creator_2,
      "<br>Influence Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  add_trace(
    data = music_by_date_3,
    x = ~release_date,
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = chosen_creator_3,
    line = list(color = "purple", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Artist: ", chosen_creator_3,
      "<br>Influence Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
    add_trace(
    data = music_by_date_4,
    x = ~release_date,
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = chosen_creator_4,
    line = list(color = "orange", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Artist: ", chosen_creator_4,
      "<br>Influence Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
    add_trace(
    data = music_by_date_5,
    x = ~release_date,
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = chosen_creator_5,
    line = list(color = "skyblue", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Artist: ", chosen_creator_5,
      "<br>Influence Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Yearly Music (Song/Album) Releases",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    ),
    annotations = list(
      list(
        x = 2017, 
        y = 2,
        text = "<b>2017:<br>Donna Caldwell <br>& Xiulan Ye</b>",
        xref = "x", yref = "y",
        xanchor = "right",
        showarrow = TRUE, arrowhead = 2,
        ax = -30, ay = -15,
        font = list(color="black", size=12)
      )
    )
  )
Note

Donna Caldwell and Xiulan Ye are at 2017 with a count of 2.

Show Code
# Visualisation

plot_ly(
  data = notable_music_by_date_1,
  x = ~release_date,
  y = ~cumulative_count,
  type = "scatter",
  mode = "lines+markers",
  name = chosen_creator_1,
  line = list(color = "#2E3192", width = 2),
  marker = list(color = "red", size = 6),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Artist: ", chosen_creator_1,
    "<br>Influence Date: ", release_date,
    "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  add_trace(
    data = notable_music_by_date_2,
    x = ~release_date,
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = chosen_creator_2,
    line = list(color = "green", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Artist: ", chosen_creator_2,
      "<br>Influence Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  add_trace(
    data = notable_music_by_date_3,
    x = ~release_date,
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = chosen_creator_3,
    line = list(color = "purple", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Artist: ", chosen_creator_3,
      "<br>Influence Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
    add_trace(
    data = notable_music_by_date_4,
    x = ~release_date,
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = chosen_creator_4,
    line = list(color = "orange", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Artist: ", chosen_creator_4,
      "<br>Influence Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
    add_trace(
    data = notable_music_by_date_5,
    x = ~release_date,
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = chosen_creator_5,
    line = list(color = "skyblue", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Artist: ", chosen_creator_5,
      "<br>Influence Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Yearly Notable Music (Song/Album) Releases",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    ),
    annotations = list(
      list(
        x = 2017, 
        y = 1,
        text = "<b>2017:<br>Donna Caldwell <br>& Xiulan Ye</b>",
        xref = "x", yref = "y",
        xanchor = "right",
        showarrow = TRUE, arrowhead = 2,
        ax = -30, ay = -15,
        font = list(color="black", size=12)
      )
    )
  )
Note

Donna Caldwell and Xiulan Ye are at 2017 with a count of 1.

Show Code
# Visualisation

plot_ly(
  data = influence_artists_by_date_1,
  x = ~creator_release_date,
  y = ~cumulative_count,
  type = "scatter",
  mode = "lines+markers",
  name = chosen_creator_1,
  line = list(color = "#2E3192", width = 2),
  marker = list(color = "red", size = 6),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Artist: ", chosen_creator_1,
    "<br>Influence Date: ", creator_release_date,
    "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  add_trace(
    data = influence_artists_by_date_2,
    x = ~creator_release_date,
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = chosen_creator_2,
    line = list(color = "green", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Artist: ", chosen_creator_2,
      "<br>Influence Date: ", creator_release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  add_trace(
    data = influence_artists_by_date_3,
    x = ~creator_release_date,
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = chosen_creator_3,
    line = list(color = "purple", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Artist: ", chosen_creator_3,
      "<br>Influence Date: ", creator_release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
    add_trace(
    data = influence_artists_by_date_4,
    x = ~creator_release_date,
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = chosen_creator_4,
    line = list(color = "orange", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Artist: ", chosen_creator_4,
      "<br>Influence Date: ", creator_release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
    add_trace(
    data = influence_artists_by_date_5,
    x = ~creator_release_date,
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = chosen_creator_5,
    line = list(color = "skyblue", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Artist: ", chosen_creator_5,
      "<br>Influence Date: ", creator_release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Annual Count of New Artist Influences & Collaborations",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    ),
    annotations = list(
      list(
        x = 2029, 
        y = 159,
        text = "<b>Donna Caldwell</b>",
        xref = "x", yref = "y",
        xanchor = "left",
        showarrow = TRUE, arrowhead = 2,
        ax = 30, ay = 30,
        font = list(color="black", size=12)
      )
    )
  )
Note

Donna Caldwell overlaps with Xiulan Ye

Show Code
# Visualisation

plot_ly(
  data = influence_song_by_date_1,
  x = ~release_date,
  y = ~cumulative_count,
  type = "scatter",
  mode = "lines+markers",
  name = chosen_creator_1,
  line = list(color = "#2E3192", width = 2),
  marker = list(color = "red", size = 6),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Artist: ", chosen_creator_1,
    "<br>Influence Date: ", release_date,
    "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  add_trace(
    data = influence_song_by_date_2,
    x = ~release_date,
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = chosen_creator_2,
    line = list(color = "green", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Artist: ", chosen_creator_2,
      "<br>Influence Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  add_trace(
    data = influence_song_by_date_3,
    x = ~release_date,
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = chosen_creator_3,
    line = list(color = "purple", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Artist: ", chosen_creator_3,
      "<br>Influence Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
    add_trace(
    data = influence_song_by_date_4,
    x = ~release_date,
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = chosen_creator_4,
    line = list(color = "orange", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Artist: ", chosen_creator_4,
      "<br>Influence Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
    add_trace(
    data = influence_song_by_date_5,
    x = ~release_date,
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = chosen_creator_5,
    line = list(color = "skyblue", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Artist: ", chosen_creator_5,
      "<br>Influence Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Yearly Music (Song/Album) Influence",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    ),
    annotations = list(
      list(
        x = 2024, 
        y = 16,
        text = "<b>Donna Caldwell</b>",
        xref = "x", yref = "y",
        xanchor = "right",
        showarrow = TRUE, arrowhead = 2,
        ax = -30, ay = -20,
        font = list(color="black", size=12)
      )
    )
  )
Note
  • Donna Caldwell overlaps with Xiulan Ye
  • Sailor Shift has no influence

6.3.8 Analysing the relationships between these five artists

Show Code
# Data Preparation

# Step 1: Find outgoing edges from Chao Wu
out_edges_1 <- mc1_edges_clean %>%
  filter(from == unique(chosen_node_1))

# Step 2: Identify neighbour node names
out_node_names_1 <- out_edges_1$to



# Step 1: Find outgoing edges from Chao Wu
out_edges_2 <- mc1_edges_clean %>%
  filter(from == unique(chosen_node_2))

# Step 2: Identify neighbour node names
out_node_names_2 <- out_edges_2$to



# Step 1: Find outgoing edges from Chao Wu
out_edges_3 <- mc1_edges_clean %>%
  filter(from == unique(chosen_node_3))

# Step 2: Identify neighbour node names
out_node_names_3 <- out_edges_3$to



# Step 1: Find outgoing edges from Chao Wu
out_edges_4 <- mc1_edges_clean %>%
  filter(from == unique(chosen_node_4))

# Step 2: Identify neighbour node names
out_node_names_4 <- out_edges_4$to



# Step 1: Find outgoing edges from Chao Wu
out_edges_5 <- mc1_edges_clean %>%
  filter(from == unique(chosen_node_5))

# Step 2: Identify neighbour node names
out_node_names_5 <- out_edges_5$to



# Build subgraph using names
sub_nodes <- unique(c(chosen_node_1,
                      chosen_node_2,
                      chosen_node_3,
                      chosen_node_4,
                      chosen_node_5,
                      out_node_names_1,
                      out_node_names_2,
                      out_node_names_3,
                      out_node_names_4,
                      out_node_names_5))

sub_graph <- graph %>%
  activate(nodes) %>%
  filter(name %in% sub_nodes)

g <- sub_graph %>%
  ggraph(layout = "fr") + 
  geom_edge_fan(
    aes(
      edge_colour = `Edge Colour`,
      start_cap = circle(1, 'mm'),
      end_cap = circle(1, 'mm')
      ),
    arrow = arrow(length = unit(1, 'mm')),
    alpha = 0.3
  ) +
  geom_point_interactive(
    aes(
      x = x,
      y = y,
      data_id = name,
      colour = `Node Colour`,
      shape = `Node Type`,
      size = ifelse(`node_name` %in% c(chosen_node_1, 
                                       chosen_node_2, 
                                       chosen_node_3, 
                                       chosen_node_4, 
                                       chosen_node_5), 3, 1),
      tooltip = case_when(
        `Node Type` == "Album" ~ sprintf(
          "%s<br/>%s<br/>Notable: %s<br/>(%s)", node_name, genre, notable, release_date
        ),
        `Node Type` == "Song" ~ sprintf(
          "%s<br/>%s<br/>Notable: %s<br/>(%s)<br/>Single: %s", node_name, genre, notable, release_date, single
        ),
        TRUE ~ sprintf("%s", node_name)
      )
    ),
    show.legend = c(size = FALSE)
  )+ 
  geom_node_text(
    aes(
      label = ifelse(`node_name` == chosen_creator_1, chosen_creator_1,
              ifelse(`node_name` == chosen_creator_2, chosen_creator_2,
              ifelse(`node_name` == chosen_creator_3, chosen_creator_3,
              ifelse(`node_name` == chosen_creator_4, chosen_creator_4,
              ifelse(`node_name` == chosen_creator_5, chosen_creator_5, NA)))))
    ),
    fontface = "bold",
    size = 2.5,
    colour = 'red',
    show.legend = FALSE
  ) +
  scale_shape_manual(
    name = "Node Type",
    values = c(
      "Album" = 16,
      "MusicalGroup" = 15,
      "Person" = 17,
      "Song" = 10
    )
  ) +
  scale_edge_colour_manual(
    name = "Edge Colour",
    values = c(
      `Creator Of` = "#47D45A",
      `Influenced By` = "#FF5757",
      `Member Of` = "#CF57FF"
    )
  ) +
  scale_colour_manual(
    name = "Node Colour",
    values = c(
      "Musician" = "grey50",
      "Oceanus Folk" = "#0027EA",
      "Other Genre" = "#A45200"
    )
  ) +
  theme_graph() +
  theme(legend.text = element_text(size = 6),
        legend.title = element_text(size = 9)) +
  scale_size_identity()

girafe(ggobj = g, width_svg = 7, height_svg = 6)
Tip
  • All five artists are solo artists and none of them are part of the same musical group
  • Chao Wu, Xia Jia, Donna Caldwell and Xiulan Ye have some collaborative works with each other which can explain why some of their graphs overlaps.
    • Chao Wu, Xia Jia, Donna Caldwell and Xiulan Ye produced Winds of the Basque Shore and Destiny’s Call together.

6.3.8 Predicting who the next three Oceanus Folk stars with be over the next five years

Sailor Shift
  • Sailor Shift is ranked 1st in Oceanus Folk star factor.
    • Per the VAST Challenge background, she is already one of music’s biggest superstars and a dominant figure in the Oceanus Folk community.
    • Therefore, she will not be included in these predictions, as she has already achieved superstar status.
  • This analysis will continue to identify the next 3 emerging artists poised to become the next Oceanus Folk stars.
Chao Wu
  • Chao Wu is the second on the Star Factor list and his influence is large having influenced/collaborated with 190 Artists and influenced the creation of 44 Music with his Oceanus Folk music.
    • Furthermore, he gained his large following despite having only released 4 music.
    • Among his 4 music,3 of them are hits.
    • His last Oceanus Folk work was in 2025 but his work is still being referred to in 2038.
Xia Jia
  • Xia Jia is the next on the Star Factor list and her influence is equally large having influenced 178 Artists and 42 Music with her Oceanus Folk music.
    • Furthermore, she has also gained this large following despite having only released 4 music.
    • Similarly, 3 of them are hits.
    • Her last Oceanus Folk work was in 2028 but her works were last referred to in 2030 which is less recent than Chao Wu.
Xiulan Ye
  • Both Xiulan Ye and Donna Caldwell have produced the 2 influential songs Basque Shore and Destiny’s Call together.
  • Their last Oceanus Folk work was in 2017 and was last referred to in 2030.
  • However, Xiulan Ye is slightly more established in the music scene having also produced Unbound from the Doom Metal genre.
  • Therefore, the third predicted star will be Xiulan Ye but Donna Caldwell has very good potential as well.

Prediction

In this part of our analysis, we set out to give three predictions of who the next Oceanus Folk stars with be over the next five years.

To do this, we designed a data-driven Star Factor score— a predictive measure using key metrics of an artist’s popularity and influence that capture what it means to be a rising star in the Oceanus Folk music scene:

Total Music Releases: How many works (songs/albums) the artist has produced.

Notable Music Releases: How many of those works are considered hits or critically notable.

Influenced Music Works: The number of downstream works (songs/albums) influenced by this artist’s creations.

Influenced Artists: The number of distinct artists who were directly or indirectly influenced by this artist.

These metrics were computed for every Oceanus Folk artist in the dataset.

library(purrr)

all_oceanus_creators <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
  pull(creator_name) %>%
  unique()

compute_creator_metrics <- function(creator_name) {
  creator_node <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
    filter(creator_name == !!creator_name) %>%
    pull(creator_from) %>%
    unique()
  
  creator_songs <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
    filter(creator_name == !!creator_name) %>%
    pull(song_to) %>%
    unique()
  
  notable_count <- mc1_nodes_clean %>%
    filter(name %in% creator_songs, notable == TRUE) %>%
    nrow()
  
  influenced_music <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
    filter(creator_from %in% creator_node, `Edge Colour` == "Influenced By") %>%
    pull(infuence_music_collaborate) %>%
    unique() %>%
    length()
  
  influenced_artists <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
    filter(creator_from %in% creator_node, influence_creator != creator_node) %>%
    pull(influence_creator) %>%
    unique() %>%
    length()
  
  tibble(
    creator_name = creator_name,
    total_music = length(creator_songs),
    notable_music = notable_count,
    influenced_music = influenced_music,
    influenced_artists = influenced_artists
  )
}

Normalize all 4 columns to 0–1 scale

artist_metrics_df <- map_dfr(all_oceanus_creators, compute_creator_metrics)

artist_metrics_df <- artist_metrics_df %>%
  mutate(
    norm_total_music = (total_music - min(total_music)) / (max(total_music) - min(total_music)),
    norm_notable_music = (notable_music - min(notable_music)) / (max(notable_music) - min(notable_music)),
    norm_influenced_music = (influenced_music - min(influenced_music)) / (max(influenced_music) - min(influenced_music)),
    norm_influenced_artists = (influenced_artists - min(influenced_artists)) / (max(influenced_artists) - min(influenced_artists)),
    
    # Star Factor = Equal weighted average of all four metrics
    star_score = 0.25 * norm_total_music +
                 0.25 * norm_notable_music +
                 0.25 * norm_influenced_music +
                 0.25 * norm_influenced_artists
  )

Filters the data to include only years 2030–2040.

Groups by artist and year.

For each (artist, year) combination, computes:

total_music: how many distinct songs they created that year. notable_music: how many were marked as notable. influenced_music: how many unique music works they influenced. influenced_artists: how many unique artists they influenced.

# Step 1: Build yearly metrics for Oceanus Folk creators (2030–2040)
artist_yearly_metrics <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_release_date >= 2030, creator_release_date <= 2040) %>%
  group_by(creator_name, creator_release_date) %>%
  summarise(
    total_music = n_distinct(song_to),
    notable_music = sum(notable == TRUE, na.rm = TRUE),
    influenced_music = n_distinct(infuence_music_collaborate[`Edge Colour` == "Influenced By"], na.rm = TRUE),
    influenced_artists = n_distinct(influence_creator[!is.na(influence_creator)], na.rm = TRUE),
    .groups = "drop"
  )

Compute the trend slope of each metric over time per artist

Fit a linear regression model for each metric across years (y ~ year). Extract the slope (rate of change per year).

For each artist:

It calculates the annual trend (i.e., slope) of each metric from 2030 to 2040. If an artist has no data for some years, NA is filled with 0.

library(purrr)

compute_trend_slope <- function(df, metric) {
  if (nrow(df) < 2) return(NA)  # Not enough years of data
  model <- lm(reformulate("creator_release_date", metric), data = df)
  coef(model)[["creator_release_date"]]
}

# Compute slope of trend over time for each artist
trend_df <- artist_yearly_metrics %>%
  group_by(creator_name) %>%
  summarise(
    trend_music = compute_trend_slope(pick(everything()), "total_music"),
    trend_notable = compute_trend_slope(pick(everything()), "notable_music"),
    trend_influenced_music = compute_trend_slope(pick(everything()), "influenced_music"),
    trend_influenced_artists = compute_trend_slope(pick(everything()), "influenced_artists"),
    .groups = "drop"
  ) %>%
  replace_na(list(
    trend_music = 0,
    trend_notable = 0,
    trend_influenced_music = 0,
    trend_influenced_artists = 0
  ))

Joins the trend slopes to the base dataset artist_metrics_df, which already contains each artist’s cumulative star_score (as of 2040).

Calculates an average trend across the 4 metrics.

Projects the star score 5 years into the future (2045) using “predicted_star_score = star_score (in 2040) + 5 × average trend per year”

# Merge current score (2040) with trend data
predicted_star_df <- artist_metrics_df %>%
  left_join(trend_df, by = "creator_name") %>%
  mutate(
    # Average slope of the 4 metrics
    trend_avg = (trend_music + trend_notable + trend_influenced_music + trend_influenced_artists) / 4,

    # Predict star score in 2045 assuming 5 years of continued trend
    predicted_star_score = star_score + 5 * trend_avg
  ) %>%
  arrange(desc(predicted_star_score))
predicted_star_df %>%
  select(creator_name, star_score, trend_avg, predicted_star_score) %>%
  head(10) %>%
  kable(format = "html", digits = 2, caption = "Top 10 Predicted Oceanus Folk Stars in 2045") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), full_width = FALSE)
Top 10 Predicted Oceanus Folk Stars in 2045
creator_name star_score trend_avg predicted_star_score
Kai Reynolds 0.05 0.75 3.80
Maya Jensen 0.10 0.37 1.97
Sophie Ramirez 0.10 0.30 1.60
Sailor Shift 0.55 0.05 0.81
Tidal Reverie 0.06 0.12 0.68
Coralia Bellweather 0.09 0.00 0.09
Levi Holloway 0.07 0.00 0.07
Jie Li 0.07 0.00 0.07
Marin Thorne 0.07 0.00 0.07
Jonah Calloway 0.07 0.00 0.07

Reference

Figueiredo, F., Panoutsos, T., & Andrade, N. (2024, October 21). Surprising patterns in musical influence networks (Version 1) [Preprint]. arXiv. Retrieved from https://doi.org/10.48550/arXiv.2410.15996

Ferwerda, B., & Schedl, M. (2016). Investigating the relationship between diversity in music consumption behavior and cultural dimensions: A cross-country analysis (SOAP’16 workshop paper, UMAP Extended Proceedings). CEUR Workshop Proceedings, 1618. Retrieved from https://ceur-ws.org/Vol-1618/SOAP_paper1.pdf